Overview

Dataset statistics

Number of variables59
Number of observations50
Missing cells1066
Missing cells (%)36.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.2 KiB
Average record size in memory474.6 B

Variable types

Numeric12
Categorical38
Unsupported9

Alerts

airdate has constant value "2020-12-27" Constant
_embedded.show.network.officialSite has constant value "https://www.hbo.com/" Constant
id is highly correlated with _embedded.show.id and 2 other fieldsHigh correlation
season is highly correlated with number and 3 other fieldsHigh correlation
number is highly correlated with season and 2 other fieldsHigh correlation
runtime is highly correlated with _embedded.show.runtime and 2 other fieldsHigh correlation
rating.average is highly correlated with _embedded.show.runtime and 2 other fieldsHigh correlation
_embedded.show.id is highly correlated with id and 4 other fieldsHigh correlation
_embedded.show.runtime is highly correlated with runtime and 4 other fieldsHigh correlation
_embedded.show.averageRuntime is highly correlated with runtime and 2 other fieldsHigh correlation
_embedded.show.rating.average is highly correlated with id and 6 other fieldsHigh correlation
_embedded.show.weight is highly correlated with rating.average and 2 other fieldsHigh correlation
_embedded.show.externals.thetvdb is highly correlated with season and 4 other fieldsHigh correlation
_embedded.show.updated is highly correlated with _embedded.show.rating.average and 1 other fieldsHigh correlation
_embedded.show.network.id is highly correlated with id and 9 other fieldsHigh correlation
id is highly correlated with rating.average and 2 other fieldsHigh correlation
season is highly correlated with number and 5 other fieldsHigh correlation
number is highly correlated with season and 1 other fieldsHigh correlation
runtime is highly correlated with season and 4 other fieldsHigh correlation
rating.average is highly correlated with id and 6 other fieldsHigh correlation
_embedded.show.id is highly correlated with season and 2 other fieldsHigh correlation
_embedded.show.runtime is highly correlated with season and 6 other fieldsHigh correlation
_embedded.show.averageRuntime is highly correlated with runtime and 3 other fieldsHigh correlation
_embedded.show.rating.average is highly correlated with id and 6 other fieldsHigh correlation
_embedded.show.weight is highly correlated with rating.average and 2 other fieldsHigh correlation
_embedded.show.webChannel.id is highly correlated with rating.average and 1 other fieldsHigh correlation
_embedded.show.externals.thetvdb is highly correlated with season and 4 other fieldsHigh correlation
_embedded.show.updated is highly correlated with _embedded.show.rating.average and 1 other fieldsHigh correlation
_embedded.show.network.id is highly correlated with id and 9 other fieldsHigh correlation
id is highly correlated with _embedded.show.rating.averageHigh correlation
season is highly correlated with _embedded.show.externals.thetvdb and 1 other fieldsHigh correlation
number is highly correlated with _embedded.show.externals.thetvdb and 1 other fieldsHigh correlation
runtime is highly correlated with _embedded.show.runtime and 2 other fieldsHigh correlation
rating.average is highly correlated with _embedded.show.runtime and 1 other fieldsHigh correlation
_embedded.show.id is highly correlated with _embedded.show.rating.average and 1 other fieldsHigh correlation
_embedded.show.runtime is highly correlated with runtime and 4 other fieldsHigh correlation
_embedded.show.averageRuntime is highly correlated with runtime and 2 other fieldsHigh correlation
_embedded.show.rating.average is highly correlated with id and 5 other fieldsHigh correlation
_embedded.show.weight is highly correlated with rating.average and 2 other fieldsHigh correlation
_embedded.show.externals.thetvdb is highly correlated with season and 4 other fieldsHigh correlation
_embedded.show.updated is highly correlated with _embedded.show.rating.average and 1 other fieldsHigh correlation
_embedded.show.network.id is highly correlated with season and 7 other fieldsHigh correlation
id is highly correlated with url and 36 other fieldsHigh correlation
url is highly correlated with id and 45 other fieldsHigh correlation
name is highly correlated with id and 41 other fieldsHigh correlation
season is highly correlated with id and 20 other fieldsHigh correlation
number is highly correlated with id and 29 other fieldsHigh correlation
type is highly correlated with id and 15 other fieldsHigh correlation
airtime is highly correlated with url and 22 other fieldsHigh correlation
airstamp is highly correlated with id and 40 other fieldsHigh correlation
runtime is highly correlated with id and 34 other fieldsHigh correlation
summary is highly correlated with id and 34 other fieldsHigh correlation
rating.average is highly correlated with url and 26 other fieldsHigh correlation
image.medium is highly correlated with id and 39 other fieldsHigh correlation
image.original is highly correlated with id and 39 other fieldsHigh correlation
_links.self.href is highly correlated with id and 45 other fieldsHigh correlation
_embedded.show.id is highly correlated with id and 42 other fieldsHigh correlation
_embedded.show.url is highly correlated with id and 45 other fieldsHigh correlation
_embedded.show.name is highly correlated with id and 45 other fieldsHigh correlation
_embedded.show.type is highly correlated with id and 35 other fieldsHigh correlation
_embedded.show.language is highly correlated with url and 40 other fieldsHigh correlation
_embedded.show.status is highly correlated with id and 30 other fieldsHigh correlation
_embedded.show.runtime is highly correlated with id and 33 other fieldsHigh correlation
_embedded.show.averageRuntime is highly correlated with id and 34 other fieldsHigh correlation
_embedded.show.premiered is highly correlated with id and 45 other fieldsHigh correlation
_embedded.show.ended is highly correlated with id and 33 other fieldsHigh correlation
_embedded.show.officialSite is highly correlated with id and 45 other fieldsHigh correlation
_embedded.show.schedule.time is highly correlated with url and 35 other fieldsHigh correlation
_embedded.show.rating.average is highly correlated with id and 30 other fieldsHigh correlation
_embedded.show.weight is highly correlated with url and 36 other fieldsHigh correlation
_embedded.show.webChannel.id is highly correlated with url and 30 other fieldsHigh correlation
_embedded.show.webChannel.name is highly correlated with id and 38 other fieldsHigh correlation
_embedded.show.webChannel.country.name is highly correlated with url and 30 other fieldsHigh correlation
_embedded.show.webChannel.country.code is highly correlated with url and 30 other fieldsHigh correlation
_embedded.show.webChannel.country.timezone is highly correlated with url and 30 other fieldsHigh correlation
_embedded.show.webChannel.officialSite is highly correlated with id and 34 other fieldsHigh correlation
_embedded.show.externals.thetvdb is highly correlated with id and 40 other fieldsHigh correlation
_embedded.show.externals.imdb is highly correlated with id and 38 other fieldsHigh correlation
_embedded.show.image.medium is highly correlated with id and 45 other fieldsHigh correlation
_embedded.show.image.original is highly correlated with id and 45 other fieldsHigh correlation
_embedded.show.summary is highly correlated with id and 40 other fieldsHigh correlation
_embedded.show.updated is highly correlated with id and 34 other fieldsHigh correlation
_embedded.show._links.self.href is highly correlated with id and 45 other fieldsHigh correlation
_embedded.show._links.previousepisode.href is highly correlated with id and 45 other fieldsHigh correlation
_embedded.show.network.id is highly correlated with id and 24 other fieldsHigh correlation
_embedded.show.network.name is highly correlated with id and 24 other fieldsHigh correlation
_embedded.show.network.country.name is highly correlated with id and 24 other fieldsHigh correlation
_embedded.show.network.country.code is highly correlated with id and 24 other fieldsHigh correlation
_embedded.show.network.country.timezone is highly correlated with id and 24 other fieldsHigh correlation
number has 2 (4.0%) missing values Missing
runtime has 3 (6.0%) missing values Missing
summary has 35 (70.0%) missing values Missing
rating.average has 39 (78.0%) missing values Missing
image.medium has 31 (62.0%) missing values Missing
image.original has 31 (62.0%) missing values Missing
_embedded.show.runtime has 18 (36.0%) missing values Missing
_embedded.show.averageRuntime has 3 (6.0%) missing values Missing
_embedded.show.ended has 28 (56.0%) missing values Missing
_embedded.show.officialSite has 5 (10.0%) missing values Missing
_embedded.show.rating.average has 39 (78.0%) missing values Missing
_embedded.show.network has 50 (100.0%) missing values Missing
_embedded.show.webChannel.id has 2 (4.0%) missing values Missing
_embedded.show.webChannel.name has 2 (4.0%) missing values Missing
_embedded.show.webChannel.country.name has 25 (50.0%) missing values Missing
_embedded.show.webChannel.country.code has 25 (50.0%) missing values Missing
_embedded.show.webChannel.country.timezone has 25 (50.0%) missing values Missing
_embedded.show.webChannel.officialSite has 28 (56.0%) missing values Missing
_embedded.show.dvdCountry has 50 (100.0%) missing values Missing
_embedded.show.externals.tvrage has 50 (100.0%) missing values Missing
_embedded.show.externals.thetvdb has 13 (26.0%) missing values Missing
_embedded.show.externals.imdb has 22 (44.0%) missing values Missing
_embedded.show.image.medium has 1 (2.0%) missing values Missing
_embedded.show.image.original has 1 (2.0%) missing values Missing
_embedded.show.summary has 6 (12.0%) missing values Missing
image has 50 (100.0%) missing values Missing
_embedded.show._links.nextepisode.href has 48 (96.0%) missing values Missing
_embedded.show.network.id has 47 (94.0%) missing values Missing
_embedded.show.network.name has 47 (94.0%) missing values Missing
_embedded.show.network.country.name has 47 (94.0%) missing values Missing
_embedded.show.network.country.code has 47 (94.0%) missing values Missing
_embedded.show.network.country.timezone has 47 (94.0%) missing values Missing
_embedded.show.network.officialSite has 49 (98.0%) missing values Missing
_embedded.show.webChannel has 50 (100.0%) missing values Missing
_embedded.show.webChannel.country has 50 (100.0%) missing values Missing
_embedded.show.image has 50 (100.0%) missing values Missing
url is uniformly distributed Uniform
summary is uniformly distributed Uniform
image.medium is uniformly distributed Uniform
image.original is uniformly distributed Uniform
_links.self.href is uniformly distributed Uniform
_embedded.show._links.nextepisode.href is uniformly distributed Uniform
_embedded.show.network.id is uniformly distributed Uniform
_embedded.show.network.name is uniformly distributed Uniform
_embedded.show.network.country.name is uniformly distributed Uniform
_embedded.show.network.country.code is uniformly distributed Uniform
_embedded.show.network.country.timezone is uniformly distributed Uniform
id has unique values Unique
url has unique values Unique
_links.self.href has unique values Unique
_embedded.show.genres is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.schedule.days is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.network is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.dvdCountry is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.externals.tvrage is an unsupported type, check if it needs cleaning or further analysis Unsupported
image is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.webChannel is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.webChannel.country is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.image is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-09-05 04:46:11.174446
Analysis finished2022-09-05 04:46:26.737314
Duration15.56 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2031250.82
Minimum1953071
Maximum2318113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-09-04T23:46:26.785313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1953071
5-th percentile1972197.65
Q11988853.5
median1993651.5
Q32014606
95-th percentile2246403.25
Maximum2318113
Range365042
Interquartile range (IQR)25752.5

Descriptive statistics

Standard deviation89063.92661
Coefficient of variation (CV)0.04384683848
Kurtosis2.722213579
Mean2031250.82
Median Absolute Deviation (MAD)9812
Skewness1.95324965
Sum101562541
Variance7932383023
MonotonicityNot monotonic
2022-09-04T23:46:27.005268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19934961
 
2.0%
20343601
 
2.0%
19936511
 
2.0%
19936521
 
2.0%
19936531
 
2.0%
19936541
 
2.0%
19947101
 
2.0%
19954911
 
2.0%
19975321
 
2.0%
19975331
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
19530711
2.0%
19563411
2.0%
19706781
2.0%
19740551
2.0%
19740561
2.0%
19751891
2.0%
19751901
2.0%
19757471
2.0%
19780141
2.0%
19816021
2.0%
ValueCountFrequency (%)
23181131
2.0%
22673181
2.0%
22559861
2.0%
22346911
2.0%
22044511
2.0%
21761441
2.0%
21659321
2.0%
21262281
2.0%
21117611
2.0%
20525121
2.0%

url
Categorical

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
https://www.tvmaze.com/episodes/1993496/troe-iz-prostokvasino-2x41-bobrovyj-meh
 
1
https://www.tvmaze.com/episodes/2034360/lulu-1x01-episode-1
 
1
https://www.tvmaze.com/episodes/1993651/outlier-1x05-episode-5
 
1
https://www.tvmaze.com/episodes/1993652/outlier-1x06-episode-6
 
1
https://www.tvmaze.com/episodes/1993653/outlier-1x07-episode-7
 
1
Other values (45)
45 

Length

Max length139
Median length102
Mean length77.8
Min length59

Characters and Unicode

Total characters3890
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/1993496/troe-iz-prostokvasino-2x41-bobrovyj-meh
2nd rowhttps://www.tvmaze.com/episodes/1993442/top-10-po-versii-seasonvarru-2x12-top-10-samyh-ozidaemyh-novinok-v-mire-serialov
3rd rowhttps://www.tvmaze.com/episodes/1956341/hero-return-1x12-episode-12
4th rowhttps://www.tvmaze.com/episodes/1988864/swallowed-star-1x06-episode-6
5th rowhttps://www.tvmaze.com/episodes/2052512/wu-shen-zhu-zai-1x87-episode-87

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1993496/troe-iz-prostokvasino-2x41-bobrovyj-meh1
 
2.0%
https://www.tvmaze.com/episodes/2034360/lulu-1x01-episode-11
 
2.0%
https://www.tvmaze.com/episodes/1993651/outlier-1x05-episode-51
 
2.0%
https://www.tvmaze.com/episodes/1993652/outlier-1x06-episode-61
 
2.0%
https://www.tvmaze.com/episodes/1993653/outlier-1x07-episode-71
 
2.0%
https://www.tvmaze.com/episodes/1993654/outlier-1x08-episode-81
 
2.0%
https://www.tvmaze.com/episodes/1994710/the-controllers-1x05-episode-51
 
2.0%
https://www.tvmaze.com/episodes/1995491/the-controllers-1x06-episode-61
 
2.0%
https://www.tvmaze.com/episodes/1997532/the-penalty-zone-1x25-episode-251
 
2.0%
https://www.tvmaze.com/episodes/1997533/the-penalty-zone-1x26-episode-261
 
2.0%
Other values (40)40
80.0%

Length

2022-09-04T23:46:27.113275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1993496/troe-iz-prostokvasino-2x41-bobrovyj-meh1
 
2.0%
https://www.tvmaze.com/episodes/1978014/30-monedas-1x05-el-doble1
 
2.0%
https://www.tvmaze.com/episodes/1993647/outlier-1x01-episode-11
 
2.0%
https://www.tvmaze.com/episodes/1956341/hero-return-1x12-episode-121
 
2.0%
https://www.tvmaze.com/episodes/1988864/swallowed-star-1x06-episode-61
 
2.0%
https://www.tvmaze.com/episodes/2052512/wu-shen-zhu-zai-1x87-episode-871
 
2.0%
https://www.tvmaze.com/episodes/2012323/mans-diary-2x08-episode-81
 
2.0%
https://www.tvmaze.com/episodes/2005757/legend-of-yun-qian-1x10-episode-101
 
2.0%
https://www.tvmaze.com/episodes/1974055/love-revolution-1x29-episode-291
 
2.0%
https://www.tvmaze.com/episodes/1974056/love-revolution-1x30-episode-301
 
2.0%
Other values (40)40
80.0%

Most occurring characters

ValueCountFrequency (%)
e333
 
8.6%
-280
 
7.2%
s254
 
6.5%
/250
 
6.4%
t232
 
6.0%
o220
 
5.7%
i163
 
4.2%
w161
 
4.1%
p155
 
4.0%
a154
 
4.0%
Other values (30)1688
43.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2636
67.8%
Decimal Number574
 
14.8%
Other Punctuation400
 
10.3%
Dash Punctuation280
 
7.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e333
12.6%
s254
 
9.6%
t232
 
8.8%
o220
 
8.3%
i163
 
6.2%
w161
 
6.1%
p155
 
5.9%
a154
 
5.8%
m126
 
4.8%
d109
 
4.1%
Other values (16)729
27.7%
Decimal Number
ValueCountFrequency (%)
1123
21.4%
276
13.2%
968
11.8%
068
11.8%
549
 
8.5%
646
 
8.0%
345
 
7.8%
737
 
6.4%
831
 
5.4%
431
 
5.4%
Other Punctuation
ValueCountFrequency (%)
/250
62.5%
.100
 
25.0%
:50
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-280
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2636
67.8%
Common1254
32.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e333
12.6%
s254
 
9.6%
t232
 
8.8%
o220
 
8.3%
i163
 
6.2%
w161
 
6.1%
p155
 
5.9%
a154
 
5.8%
m126
 
4.8%
d109
 
4.1%
Other values (16)729
27.7%
Common
ValueCountFrequency (%)
-280
22.3%
/250
19.9%
1123
9.8%
.100
 
8.0%
276
 
6.1%
968
 
5.4%
068
 
5.4%
:50
 
4.0%
549
 
3.9%
646
 
3.7%
Other values (4)144
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII3890
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e333
 
8.6%
-280
 
7.2%
s254
 
6.5%
/250
 
6.4%
t232
 
6.0%
o220
 
5.7%
i163
 
4.2%
w161
 
4.1%
p155
 
4.0%
a154
 
4.0%
Other values (30)1688
43.4%

name
Categorical

HIGH CORRELATION

Distinct39
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
Episode 5
Episode 6
Episode 1
Episode 8
 
2
Episode 3
 
2
Other values (34)
34 

Length

Max length82
Median length61
Mean length18.52
Min length6

Characters and Unicode

Total characters926
Distinct characters106
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)68.0%

Sample

1st rowБобровый мех
2nd rowТОП-10 самых ожидаемых новинок в мире сериалов
3rd rowEpisode 12
4th rowEpisode 6
5th rowEpisode 87

Common Values

ValueCountFrequency (%)
Episode 54
 
8.0%
Episode 64
 
8.0%
Episode 14
 
8.0%
Episode 82
 
4.0%
Episode 32
 
4.0%
Бобровый мех1
 
2.0%
Постдеконструкция с Владимиром Сурдиным. Фильм "Интерстеллар"1
 
2.0%
Episode 41
 
2.0%
Episode 71
 
2.0%
Episode 251
 
2.0%
Other values (29)29
58.0%

Length

2022-09-04T23:46:27.210267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
episode27
 
16.6%
66
 
3.7%
54
 
2.5%
14
 
2.5%
the3
 
1.8%
of2
 
1.2%
72
 
1.2%
de2
 
1.2%
32
 
1.2%
82
 
1.2%
Other values (109)109
66.9%

Most occurring characters

ValueCountFrequency (%)
113
 
12.2%
e62
 
6.7%
s51
 
5.5%
i50
 
5.4%
o42
 
4.5%
d39
 
4.2%
a33
 
3.6%
E32
 
3.5%
p29
 
3.1%
n22
 
2.4%
Other values (96)453
48.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter631
68.1%
Space Separator113
 
12.2%
Uppercase Letter103
 
11.1%
Decimal Number55
 
5.9%
Other Punctuation22
 
2.4%
Dash Punctuation2
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e62
 
9.8%
s51
 
8.1%
i50
 
7.9%
o42
 
6.7%
d39
 
6.2%
a33
 
5.2%
p29
 
4.6%
n22
 
3.5%
о21
 
3.3%
r19
 
3.0%
Other values (42)263
41.7%
Uppercase Letter
ValueCountFrequency (%)
E32
31.1%
T8
 
7.8%
N5
 
4.9%
C5
 
4.9%
O5
 
4.9%
R4
 
3.9%
A3
 
2.9%
M3
 
2.9%
G3
 
2.9%
U2
 
1.9%
Other values (24)33
32.0%
Decimal Number
ValueCountFrequency (%)
212
21.8%
68
14.5%
18
14.5%
07
12.7%
56
10.9%
75
9.1%
34
 
7.3%
83
 
5.5%
41
 
1.8%
91
 
1.8%
Other Punctuation
ValueCountFrequency (%)
"6
27.3%
,5
22.7%
'3
13.6%
:2
 
9.1%
/2
 
9.1%
.2
 
9.1%
!1
 
4.5%
#1
 
4.5%
Space Separator
ValueCountFrequency (%)
113
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin522
56.4%
Cyrillic212
22.9%
Common192
 
20.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e62
11.9%
s51
 
9.8%
i50
 
9.6%
o42
 
8.0%
d39
 
7.5%
a33
 
6.3%
E32
 
6.1%
p29
 
5.6%
n22
 
4.2%
r19
 
3.6%
Other values (39)143
27.4%
Cyrillic
ValueCountFrequency (%)
о21
 
9.9%
е17
 
8.0%
а16
 
7.5%
р15
 
7.1%
и13
 
6.1%
с11
 
5.2%
н11
 
5.2%
в10
 
4.7%
м10
 
4.7%
т10
 
4.7%
Other values (27)78
36.8%
Common
ValueCountFrequency (%)
113
58.9%
212
 
6.2%
68
 
4.2%
18
 
4.2%
07
 
3.6%
"6
 
3.1%
56
 
3.1%
,5
 
2.6%
75
 
2.6%
34
 
2.1%
Other values (10)18
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII711
76.8%
Cyrillic212
 
22.9%
None3
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
113
15.9%
e62
 
8.7%
s51
 
7.2%
i50
 
7.0%
o42
 
5.9%
d39
 
5.5%
a33
 
4.6%
E32
 
4.5%
p29
 
4.1%
n22
 
3.1%
Other values (56)238
33.5%
Cyrillic
ValueCountFrequency (%)
о21
 
9.9%
е17
 
8.0%
а16
 
7.5%
р15
 
7.1%
и13
 
6.1%
с11
 
5.2%
н11
 
5.2%
в10
 
4.7%
м10
 
4.7%
т10
 
4.7%
Other values (27)78
36.8%
None
ValueCountFrequency (%)
ă1
33.3%
é1
33.3%
ä1
33.3%

season
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.48
Minimum1
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-09-04T23:46:27.288838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile1132.6
Maximum2020
Range2019
Interquartile range (IQR)1

Descriptive statistics

Standard deviation484.0576471
Coefficient of variation (CV)3.920129957
Kurtosis13.11824218
Mean123.48
Median Absolute Deviation (MAD)0
Skewness3.819857605
Sum6174
Variance234311.8057
MonotonicityNot monotonic
2022-09-04T23:46:27.355821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
134
68.0%
29
 
18.0%
20203
 
6.0%
61
 
2.0%
51
 
2.0%
31
 
2.0%
481
 
2.0%
ValueCountFrequency (%)
134
68.0%
29
 
18.0%
31
 
2.0%
51
 
2.0%
61
 
2.0%
481
 
2.0%
20203
 
6.0%
ValueCountFrequency (%)
20203
 
6.0%
481
 
2.0%
61
 
2.0%
51
 
2.0%
31
 
2.0%
29
 
18.0%
134
68.0%

number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct25
Distinct (%)52.1%
Missing2
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean22.97916667
Minimum1
Maximum354
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-09-04T23:46:27.432832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median7
Q325.25
95-th percentile55.25
Maximum354
Range353
Interquartile range (IQR)20.25

Descriptive statistics

Standard deviation52.23575428
Coefficient of variation (CV)2.27317879
Kurtosis35.86242737
Mean22.97916667
Median Absolute Deviation (MAD)4
Skewness5.680323477
Sum1103
Variance2728.574025
MonotonicityNot monotonic
2022-09-04T23:46:27.511821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
68
16.0%
56
 
12.0%
15
 
10.0%
83
 
6.0%
112
 
4.0%
122
 
4.0%
32
 
4.0%
72
 
4.0%
522
 
4.0%
41
 
2.0%
Other values (15)15
30.0%
(Missing)2
 
4.0%
ValueCountFrequency (%)
15
10.0%
21
 
2.0%
32
 
4.0%
41
 
2.0%
56
12.0%
68
16.0%
72
 
4.0%
83
 
6.0%
101
 
2.0%
112
 
4.0%
ValueCountFrequency (%)
3541
2.0%
871
2.0%
571
2.0%
522
4.0%
491
2.0%
411
2.0%
381
2.0%
371
2.0%
301
2.0%
291
2.0%

type
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
regular
48 
insignificant_special
 
1
significant_special
 
1

Length

Max length21
Median length7
Mean length7.52
Min length7

Characters and Unicode

Total characters376
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)4.0%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular48
96.0%
insignificant_special1
 
2.0%
significant_special1
 
2.0%

Length

2022-09-04T23:46:27.595840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:46:27.672908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
regular48
96.0%
insignificant_special1
 
2.0%
significant_special1
 
2.0%

Most occurring characters

ValueCountFrequency (%)
r96
25.5%
a52
13.8%
e50
13.3%
g50
13.3%
l50
13.3%
u48
12.8%
i9
 
2.4%
n5
 
1.3%
s4
 
1.1%
c4
 
1.1%
Other values (4)8
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter374
99.5%
Connector Punctuation2
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r96
25.7%
a52
13.9%
e50
13.4%
g50
13.4%
l50
13.4%
u48
12.8%
i9
 
2.4%
n5
 
1.3%
s4
 
1.1%
c4
 
1.1%
Other values (3)6
 
1.6%
Connector Punctuation
ValueCountFrequency (%)
_2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin374
99.5%
Common2
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r96
25.7%
a52
13.9%
e50
13.4%
g50
13.4%
l50
13.4%
u48
12.8%
i9
 
2.4%
n5
 
1.3%
s4
 
1.1%
c4
 
1.1%
Other values (3)6
 
1.6%
Common
ValueCountFrequency (%)
_2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII376
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r96
25.5%
a52
13.8%
e50
13.3%
g50
13.3%
l50
13.3%
u48
12.8%
i9
 
2.4%
n5
 
1.3%
s4
 
1.1%
c4
 
1.1%
Other values (4)8
 
2.1%

airdate
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
2020-12-27
50 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters500
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-27
2nd row2020-12-27
3rd row2020-12-27
4th row2020-12-27
5th row2020-12-27

Common Values

ValueCountFrequency (%)
2020-12-2750
100.0%

Length

2022-09-04T23:46:27.739259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:46:27.807247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-2750
100.0%

Most occurring characters

ValueCountFrequency (%)
2200
40.0%
0100
20.0%
-100
20.0%
150
 
10.0%
750
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number400
80.0%
Dash Punctuation100
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2200
50.0%
0100
25.0%
150
 
12.5%
750
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2200
40.0%
0100
20.0%
-100
20.0%
150
 
10.0%
750
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2200
40.0%
0100
20.0%
-100
20.0%
150
 
10.0%
750
 
10.0%

airtime
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
37 
20:00
10:00
 
3
17:00
 
2
18:00
 
1
Other values (2)
 
2

Length

Max length5
Median length0
Mean length1.3
Min length0

Characters and Unicode

Total characters65
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)6.0%

Sample

1st row
2nd row
3rd row10:00
4th row10:00
5th row10:00

Common Values

ValueCountFrequency (%)
37
74.0%
20:005
 
10.0%
10:003
 
6.0%
17:002
 
4.0%
18:001
 
2.0%
12:001
 
2.0%
12:151
 
2.0%

Length

2022-09-04T23:46:27.868804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:46:27.944904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
20:005
38.5%
10:003
23.1%
17:002
 
15.4%
18:001
 
7.7%
12:001
 
7.7%
12:151
 
7.7%

Most occurring characters

ValueCountFrequency (%)
032
49.2%
:13
20.0%
19
 
13.8%
27
 
10.8%
72
 
3.1%
81
 
1.5%
51
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number52
80.0%
Other Punctuation13
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
032
61.5%
19
 
17.3%
27
 
13.5%
72
 
3.8%
81
 
1.9%
51
 
1.9%
Other Punctuation
ValueCountFrequency (%)
:13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common65
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
032
49.2%
:13
20.0%
19
 
13.8%
27
 
10.8%
72
 
3.1%
81
 
1.5%
51
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII65
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
032
49.2%
:13
20.0%
19
 
13.8%
27
 
10.8%
72
 
3.1%
81
 
1.5%
51
 
1.5%

airstamp
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
2020-12-27T12:00:00+00:00
27 
2020-12-27T11:00:00+00:00
2020-12-27T17:00:00+00:00
2020-12-27T02:00:00+00:00
2020-12-27T00:00:00+00:00
 
2
Other values (6)

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters1250
Distinct characters12
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)8.0%

Sample

1st row2020-12-27T00:00:00+00:00
2nd row2020-12-27T00:00:00+00:00
3rd row2020-12-27T02:00:00+00:00
4th row2020-12-27T02:00:00+00:00
5th row2020-12-27T02:00:00+00:00

Common Values

ValueCountFrequency (%)
2020-12-27T12:00:00+00:0027
54.0%
2020-12-27T11:00:00+00:005
 
10.0%
2020-12-27T17:00:00+00:005
 
10.0%
2020-12-27T02:00:00+00:003
 
6.0%
2020-12-27T00:00:00+00:002
 
4.0%
2020-12-27T04:00:00+00:002
 
4.0%
2020-12-27T08:00:00+00:002
 
4.0%
2020-12-27T09:00:00+00:001
 
2.0%
2020-12-27T14:00:00+00:001
 
2.0%
2020-12-27T17:15:00+00:001
 
2.0%

Length

2022-09-04T23:46:28.016945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-27t12:00:00+00:0027
54.0%
2020-12-27t11:00:00+00:005
 
10.0%
2020-12-27t17:00:00+00:005
 
10.0%
2020-12-27t02:00:00+00:003
 
6.0%
2020-12-27t00:00:00+00:002
 
4.0%
2020-12-27t04:00:00+00:002
 
4.0%
2020-12-27t08:00:00+00:002
 
4.0%
2020-12-27t09:00:00+00:001
 
2.0%
2020-12-27t14:00:00+00:001
 
2.0%
2020-12-27t17:15:00+00:001
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0511
40.9%
2230
18.4%
:150
 
12.0%
-100
 
8.0%
196
 
7.7%
755
 
4.4%
T50
 
4.0%
+50
 
4.0%
43
 
0.2%
83
 
0.2%
Other values (2)2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number900
72.0%
Other Punctuation150
 
12.0%
Dash Punctuation100
 
8.0%
Uppercase Letter50
 
4.0%
Math Symbol50
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0511
56.8%
2230
25.6%
196
 
10.7%
755
 
6.1%
43
 
0.3%
83
 
0.3%
91
 
0.1%
51
 
0.1%
Other Punctuation
ValueCountFrequency (%)
:150
100.0%
Dash Punctuation
ValueCountFrequency (%)
-100
100.0%
Uppercase Letter
ValueCountFrequency (%)
T50
100.0%
Math Symbol
ValueCountFrequency (%)
+50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1200
96.0%
Latin50
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0511
42.6%
2230
19.2%
:150
 
12.5%
-100
 
8.3%
196
 
8.0%
755
 
4.6%
+50
 
4.2%
43
 
0.2%
83
 
0.2%
91
 
0.1%
Latin
ValueCountFrequency (%)
T50
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0511
40.9%
2230
18.4%
:150
 
12.0%
-100
 
8.0%
196
 
7.7%
755
 
4.4%
T50
 
4.0%
+50
 
4.0%
43
 
0.2%
83
 
0.2%
Other values (2)2
 
0.2%

runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct24
Distinct (%)51.1%
Missing3
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean36.57446809
Minimum4
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-09-04T23:46:28.085945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8.9
Q120
median42
Q345
95-th percentile62.8
Maximum120
Range116
Interquartile range (IQR)25

Descriptive statistics

Standard deviation23.37012649
Coefficient of variation (CV)0.63897379
Kurtosis5.344035273
Mean36.57446809
Median Absolute Deviation (MAD)12
Skewness1.819461013
Sum1719
Variance546.1628122
MonotonicityNot monotonic
2022-09-04T23:46:28.163950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
459
18.0%
424
 
8.0%
153
 
6.0%
203
 
6.0%
433
 
6.0%
1202
 
4.0%
302
 
4.0%
122
 
4.0%
442
 
4.0%
252
 
4.0%
Other values (14)15
30.0%
(Missing)3
 
6.0%
ValueCountFrequency (%)
41
 
2.0%
71
 
2.0%
81
 
2.0%
111
 
2.0%
122
4.0%
153
6.0%
191
 
2.0%
203
6.0%
211
 
2.0%
222
4.0%
ValueCountFrequency (%)
1202
 
4.0%
641
 
2.0%
601
 
2.0%
591
 
2.0%
501
 
2.0%
459
18.0%
442
 
4.0%
433
 
6.0%
424
8.0%
401
 
2.0%

summary
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct15
Distinct (%)100.0%
Missing35
Missing (%)70.0%
Memory size528.0 B
<p>After mysteriously disappearing two years ago, Elena's husband miraculously returns. In Rome, Santoro tries to recruit Vergara, offering him unimaginable power…</p>
 
1
<p>A teenage girl, Sofie, disappears on her way home from a party in Kautokeino. A friend reports her missing to the police, but the case is not taken seriously. Not until Sofie is found murdered in a village a few hours away.</p>
 
1
<p>Maja defies all warnings and begins to investigate on her own accord. And she grows increasingly confident in her theory - the police are wrong.</p>
 
1
<p>Maja's theory of a possible serial killer is finally heard by the police, who permit her to join the investigation despite their doubts. She begins to dig into old cases in the archives, searching for a pattern.</p><p> </p>
 
1
<p>Maja and the other investigators are looking for connections between several murders and missing person cases. They discover that the pattern much more extensive than first thought.</p><p> </p>
 
1
Other values (10)
10 

Length

Max length234
Median length167
Mean length171.5333333
Min length94

Characters and Unicode

Total characters2573
Distinct characters62
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)100.0%

Sample

1st row<p>After mysteriously disappearing two years ago, Elena's husband miraculously returns. In Rome, Santoro tries to recruit Vergara, offering him unimaginable power…</p>
2nd row<p>A teenage girl, Sofie, disappears on her way home from a party in Kautokeino. A friend reports her missing to the police, but the case is not taken seriously. Not until Sofie is found murdered in a village a few hours away.</p>
3rd row<p>Maja defies all warnings and begins to investigate on her own accord. And she grows increasingly confident in her theory - the police are wrong.</p>
4th row<p>Maja's theory of a possible serial killer is finally heard by the police, who permit her to join the investigation despite their doubts. She begins to dig into old cases in the archives, searching for a pattern.</p><p> </p>
5th row<p>Maja and the other investigators are looking for connections between several murders and missing person cases. They discover that the pattern much more extensive than first thought.</p><p> </p>

Common Values

ValueCountFrequency (%)
<p>After mysteriously disappearing two years ago, Elena's husband miraculously returns. In Rome, Santoro tries to recruit Vergara, offering him unimaginable power…</p>1
 
2.0%
<p>A teenage girl, Sofie, disappears on her way home from a party in Kautokeino. A friend reports her missing to the police, but the case is not taken seriously. Not until Sofie is found murdered in a village a few hours away.</p>1
 
2.0%
<p>Maja defies all warnings and begins to investigate on her own accord. And she grows increasingly confident in her theory - the police are wrong.</p>1
 
2.0%
<p>Maja's theory of a possible serial killer is finally heard by the police, who permit her to join the investigation despite their doubts. She begins to dig into old cases in the archives, searching for a pattern.</p><p> </p>1
 
2.0%
<p>Maja and the other investigators are looking for connections between several murders and missing person cases. They discover that the pattern much more extensive than first thought.</p><p> </p>1
 
2.0%
<p>Maja is reduced to the sidelines and is close to giving up. But information about Maja's own family sheds new light on the murder. Could the killer have been a part of Maja's inner circle</p><p> </p>1
 
2.0%
<p>Maja's confrontation with her father has major consequences. The day after their feud, he is found dead at home having taken his own life. The shock of the loss of her father sends Maja even closer to the tipping point.</p><p> </p>1
 
2.0%
<p>Despite all the warnings about dragging her private trauma into the case, Maja decides to try one last time to talk to her demented mother.</p><p> </p>1
 
2.0%
<p>After going down what turns out to be a blind alley, the suspicion falls on a man who was close to Maja's family in his youth. The police strike, but he escapes and disappears.</p><p> </p>1
 
2.0%
<p>In 1986, police discover a grisly crime scene: a family shot in their beds at point-blank range. The mystery deepens when a sole survivor appears.</p>1
 
2.0%
Other values (5)5
 
10.0%
(Missing)35
70.0%

Length

2022-09-04T23:46:28.248954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the26
 
6.1%
to17
 
4.0%
a14
 
3.3%
and9
 
2.1%
her9
 
2.1%
on8
 
1.9%
is8
 
1.9%
in8
 
1.9%
p7
 
1.6%
of6
 
1.4%
Other values (251)317
73.9%

Most occurring characters

ValueCountFrequency (%)
407
15.8%
e240
 
9.3%
a162
 
6.3%
t152
 
5.9%
i145
 
5.6%
o138
 
5.4%
n136
 
5.3%
r128
 
5.0%
s126
 
4.9%
h102
 
4.0%
Other values (52)837
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1926
74.9%
Space Separator414
 
16.1%
Math Symbol84
 
3.3%
Other Punctuation81
 
3.1%
Uppercase Letter61
 
2.4%
Decimal Number5
 
0.2%
Dash Punctuation2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e240
12.5%
a162
 
8.4%
t152
 
7.9%
i145
 
7.5%
o138
 
7.2%
n136
 
7.1%
r128
 
6.6%
s126
 
6.5%
h102
 
5.3%
p86
 
4.5%
Other values (15)511
26.5%
Uppercase Letter
ValueCountFrequency (%)
M12
19.7%
T8
13.1%
A5
8.2%
S5
8.2%
Y4
 
6.6%
W4
 
6.6%
N4
 
6.6%
I3
 
4.9%
E2
 
3.3%
R2
 
3.3%
Other values (9)12
19.7%
Other Punctuation
ValueCountFrequency (%)
.27
33.3%
/21
25.9%
,18
22.2%
'9
 
11.1%
"2
 
2.5%
:2
 
2.5%
1
 
1.2%
?1
 
1.2%
Decimal Number
ValueCountFrequency (%)
71
20.0%
61
20.0%
81
20.0%
91
20.0%
11
20.0%
Space Separator
ValueCountFrequency (%)
407
98.3%
 7
 
1.7%
Math Symbol
ValueCountFrequency (%)
>42
50.0%
<42
50.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1987
77.2%
Common586
 
22.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e240
12.1%
a162
 
8.2%
t152
 
7.6%
i145
 
7.3%
o138
 
6.9%
n136
 
6.8%
r128
 
6.4%
s126
 
6.3%
h102
 
5.1%
p86
 
4.3%
Other values (34)572
28.8%
Common
ValueCountFrequency (%)
407
69.5%
>42
 
7.2%
<42
 
7.2%
.27
 
4.6%
/21
 
3.6%
,18
 
3.1%
'9
 
1.5%
 7
 
1.2%
"2
 
0.3%
-2
 
0.3%
Other values (8)9
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2565
99.7%
None7
 
0.3%
Punctuation1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
407
15.9%
e240
 
9.4%
a162
 
6.3%
t152
 
5.9%
i145
 
5.7%
o138
 
5.4%
n136
 
5.3%
r128
 
5.0%
s126
 
4.9%
h102
 
4.0%
Other values (50)829
32.3%
None
ValueCountFrequency (%)
 7
100.0%
Punctuation
ValueCountFrequency (%)
1
100.0%

rating.average
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7
Distinct (%)63.6%
Missing39
Missing (%)78.0%
Infinite0
Infinite (%)0.0%
Mean7.209090909
Minimum5.5
Maximum8.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-09-04T23:46:28.321182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5.5
5-th percentile6.1
Q17
median7
Q37.6
95-th percentile8.2
Maximum8.4
Range2.9
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.7621739243
Coefficient of variation (CV)0.1057239996
Kurtosis1.854079511
Mean7.209090909
Median Absolute Deviation (MAD)0.5
Skewness-0.7755456587
Sum79.3
Variance0.5809090909
MonotonicityNot monotonic
2022-09-04T23:46:28.385182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
74
 
8.0%
7.52
 
4.0%
8.41
 
2.0%
5.51
 
2.0%
7.71
 
2.0%
6.71
 
2.0%
81
 
2.0%
(Missing)39
78.0%
ValueCountFrequency (%)
5.51
 
2.0%
6.71
 
2.0%
74
8.0%
7.52
4.0%
7.71
 
2.0%
81
 
2.0%
8.41
 
2.0%
ValueCountFrequency (%)
8.41
 
2.0%
81
 
2.0%
7.71
 
2.0%
7.52
4.0%
74
8.0%
6.71
 
2.0%
5.51
 
2.0%

image.medium
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct19
Distinct (%)100.0%
Missing31
Missing (%)62.0%
Memory size528.0 B
https://static.tvmaze.com/uploads/images/medium_landscape/291/727650.jpg
 
1
https://static.tvmaze.com/uploads/images/medium_landscape/393/983421.jpg
 
1
https://static.tvmaze.com/uploads/images/medium_landscape/389/973511.jpg
 
1
https://static.tvmaze.com/uploads/images/medium_landscape/290/727363.jpg
 
1
https://static.tvmaze.com/uploads/images/medium_landscape/290/727362.jpg
 
1
Other values (14)
14 

Length

Max length73
Median length72
Mean length72.05263158
Min length72

Characters and Unicode

Total characters1369
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)100.0%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/medium_landscape/291/727650.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/medium_landscape/294/735227.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/medium_landscape/291/729728.jpg
4th rowhttps://static.tvmaze.com/uploads/images/medium_landscape/401/1003924.jpg
5th rowhttps://static.tvmaze.com/uploads/images/medium_landscape/290/727176.jpg

Common Values

ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_landscape/291/727650.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/medium_landscape/393/983421.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/medium_landscape/389/973511.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/medium_landscape/290/727363.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/medium_landscape/290/727362.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/medium_landscape/290/727361.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/medium_landscape/290/727360.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/medium_landscape/290/727359.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/medium_landscape/290/727358.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/medium_landscape/290/727357.jpg1
 
2.0%
Other values (9)9
 
18.0%
(Missing)31
62.0%

Length

2022-09-04T23:46:28.467182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_landscape/291/727650.jpg1
 
5.3%
https://static.tvmaze.com/uploads/images/medium_landscape/290/727356.jpg1
 
5.3%
https://static.tvmaze.com/uploads/images/medium_landscape/294/735227.jpg1
 
5.3%
https://static.tvmaze.com/uploads/images/medium_landscape/291/729728.jpg1
 
5.3%
https://static.tvmaze.com/uploads/images/medium_landscape/401/1003924.jpg1
 
5.3%
https://static.tvmaze.com/uploads/images/medium_landscape/290/727176.jpg1
 
5.3%
https://static.tvmaze.com/uploads/images/medium_landscape/310/776222.jpg1
 
5.3%
https://static.tvmaze.com/uploads/images/medium_landscape/290/727116.jpg1
 
5.3%
https://static.tvmaze.com/uploads/images/medium_landscape/291/728459.jpg1
 
5.3%
https://static.tvmaze.com/uploads/images/medium_landscape/290/727357.jpg1
 
5.3%
Other values (9)9
47.4%

Most occurring characters

ValueCountFrequency (%)
/133
 
9.7%
a114
 
8.3%
s95
 
6.9%
m95
 
6.9%
t95
 
6.9%
p76
 
5.6%
e76
 
5.6%
i57
 
4.2%
c57
 
4.2%
.57
 
4.2%
Other values (22)514
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter969
70.8%
Other Punctuation209
 
15.3%
Decimal Number172
 
12.6%
Connector Punctuation19
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a114
11.8%
s95
9.8%
m95
9.8%
t95
9.8%
p76
 
7.8%
e76
 
7.8%
i57
 
5.9%
c57
 
5.9%
d57
 
5.9%
l38
 
3.9%
Other values (8)209
21.6%
Decimal Number
ValueCountFrequency (%)
238
22.1%
732
18.6%
923
13.4%
318
10.5%
016
9.3%
113
 
7.6%
510
 
5.8%
69
 
5.2%
87
 
4.1%
46
 
3.5%
Other Punctuation
ValueCountFrequency (%)
/133
63.6%
.57
27.3%
:19
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin969
70.8%
Common400
29.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a114
11.8%
s95
9.8%
m95
9.8%
t95
9.8%
p76
 
7.8%
e76
 
7.8%
i57
 
5.9%
c57
 
5.9%
d57
 
5.9%
l38
 
3.9%
Other values (8)209
21.6%
Common
ValueCountFrequency (%)
/133
33.2%
.57
14.2%
238
 
9.5%
732
 
8.0%
923
 
5.8%
_19
 
4.8%
:19
 
4.8%
318
 
4.5%
016
 
4.0%
113
 
3.2%
Other values (4)32
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1369
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/133
 
9.7%
a114
 
8.3%
s95
 
6.9%
m95
 
6.9%
t95
 
6.9%
p76
 
5.6%
e76
 
5.6%
i57
 
4.2%
c57
 
4.2%
.57
 
4.2%
Other values (22)514
37.5%

image.original
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct19
Distinct (%)100.0%
Missing31
Missing (%)62.0%
Memory size528.0 B
https://static.tvmaze.com/uploads/images/original_untouched/291/727650.jpg
 
1
https://static.tvmaze.com/uploads/images/original_untouched/393/983421.jpg
 
1
https://static.tvmaze.com/uploads/images/original_untouched/389/973511.jpg
 
1
https://static.tvmaze.com/uploads/images/original_untouched/290/727363.jpg
 
1
https://static.tvmaze.com/uploads/images/original_untouched/290/727362.jpg
 
1
Other values (14)
14 

Length

Max length75
Median length74
Mean length74.05263158
Min length74

Characters and Unicode

Total characters1407
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)100.0%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/original_untouched/291/727650.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/original_untouched/294/735227.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/original_untouched/291/729728.jpg
4th rowhttps://static.tvmaze.com/uploads/images/original_untouched/401/1003924.jpg
5th rowhttps://static.tvmaze.com/uploads/images/original_untouched/290/727176.jpg

Common Values

ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/291/727650.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/original_untouched/393/983421.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/original_untouched/389/973511.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/original_untouched/290/727363.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/original_untouched/290/727362.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/original_untouched/290/727361.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/original_untouched/290/727360.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/original_untouched/290/727359.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/original_untouched/290/727358.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/original_untouched/290/727357.jpg1
 
2.0%
Other values (9)9
 
18.0%
(Missing)31
62.0%

Length

2022-09-04T23:46:28.546479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/291/727650.jpg1
 
5.3%
https://static.tvmaze.com/uploads/images/original_untouched/290/727356.jpg1
 
5.3%
https://static.tvmaze.com/uploads/images/original_untouched/294/735227.jpg1
 
5.3%
https://static.tvmaze.com/uploads/images/original_untouched/291/729728.jpg1
 
5.3%
https://static.tvmaze.com/uploads/images/original_untouched/401/1003924.jpg1
 
5.3%
https://static.tvmaze.com/uploads/images/original_untouched/290/727176.jpg1
 
5.3%
https://static.tvmaze.com/uploads/images/original_untouched/310/776222.jpg1
 
5.3%
https://static.tvmaze.com/uploads/images/original_untouched/290/727116.jpg1
 
5.3%
https://static.tvmaze.com/uploads/images/original_untouched/291/728459.jpg1
 
5.3%
https://static.tvmaze.com/uploads/images/original_untouched/290/727357.jpg1
 
5.3%
Other values (9)9
47.4%

Most occurring characters

ValueCountFrequency (%)
/133
 
9.5%
t114
 
8.1%
a95
 
6.8%
s76
 
5.4%
o76
 
5.4%
i76
 
5.4%
m57
 
4.1%
u57
 
4.1%
e57
 
4.1%
g57
 
4.1%
Other values (23)609
43.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1007
71.6%
Other Punctuation209
 
14.9%
Decimal Number172
 
12.2%
Connector Punctuation19
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t114
 
11.3%
a95
 
9.4%
s76
 
7.5%
o76
 
7.5%
i76
 
7.5%
m57
 
5.7%
u57
 
5.7%
e57
 
5.7%
g57
 
5.7%
c57
 
5.7%
Other values (9)285
28.3%
Decimal Number
ValueCountFrequency (%)
238
22.1%
732
18.6%
923
13.4%
318
10.5%
016
9.3%
113
 
7.6%
510
 
5.8%
69
 
5.2%
87
 
4.1%
46
 
3.5%
Other Punctuation
ValueCountFrequency (%)
/133
63.6%
.57
27.3%
:19
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1007
71.6%
Common400
 
28.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t114
 
11.3%
a95
 
9.4%
s76
 
7.5%
o76
 
7.5%
i76
 
7.5%
m57
 
5.7%
u57
 
5.7%
e57
 
5.7%
g57
 
5.7%
c57
 
5.7%
Other values (9)285
28.3%
Common
ValueCountFrequency (%)
/133
33.2%
.57
14.2%
238
 
9.5%
732
 
8.0%
923
 
5.8%
_19
 
4.8%
:19
 
4.8%
318
 
4.5%
016
 
4.0%
113
 
3.2%
Other values (4)32
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1407
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/133
 
9.5%
t114
 
8.1%
a95
 
6.8%
s76
 
5.4%
o76
 
5.4%
i76
 
5.4%
m57
 
4.1%
u57
 
4.1%
e57
 
4.1%
g57
 
4.1%
Other values (23)609
43.3%

_links.self.href
Categorical

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
https://api.tvmaze.com/episodes/1993496
 
1
https://api.tvmaze.com/episodes/2034360
 
1
https://api.tvmaze.com/episodes/1993651
 
1
https://api.tvmaze.com/episodes/1993652
 
1
https://api.tvmaze.com/episodes/1993653
 
1
Other values (45)
45 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters1950
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/1993496
2nd rowhttps://api.tvmaze.com/episodes/1993442
3rd rowhttps://api.tvmaze.com/episodes/1956341
4th rowhttps://api.tvmaze.com/episodes/1988864
5th rowhttps://api.tvmaze.com/episodes/2052512

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19934961
 
2.0%
https://api.tvmaze.com/episodes/20343601
 
2.0%
https://api.tvmaze.com/episodes/19936511
 
2.0%
https://api.tvmaze.com/episodes/19936521
 
2.0%
https://api.tvmaze.com/episodes/19936531
 
2.0%
https://api.tvmaze.com/episodes/19936541
 
2.0%
https://api.tvmaze.com/episodes/19947101
 
2.0%
https://api.tvmaze.com/episodes/19954911
 
2.0%
https://api.tvmaze.com/episodes/19975321
 
2.0%
https://api.tvmaze.com/episodes/19975331
 
2.0%
Other values (40)40
80.0%

Length

2022-09-04T23:46:28.618548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19934961
 
2.0%
https://api.tvmaze.com/episodes/19780141
 
2.0%
https://api.tvmaze.com/episodes/19936471
 
2.0%
https://api.tvmaze.com/episodes/19563411
 
2.0%
https://api.tvmaze.com/episodes/19888641
 
2.0%
https://api.tvmaze.com/episodes/20525121
 
2.0%
https://api.tvmaze.com/episodes/20123231
 
2.0%
https://api.tvmaze.com/episodes/20057571
 
2.0%
https://api.tvmaze.com/episodes/19740551
 
2.0%
https://api.tvmaze.com/episodes/19740561
 
2.0%
Other values (40)40
80.0%

Most occurring characters

ValueCountFrequency (%)
/200
 
10.3%
p150
 
7.7%
s150
 
7.7%
e150
 
7.7%
t150
 
7.7%
o100
 
5.1%
a100
 
5.1%
i100
 
5.1%
.100
 
5.1%
m100
 
5.1%
Other values (16)650
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1250
64.1%
Other Punctuation350
 
17.9%
Decimal Number350
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p150
12.0%
s150
12.0%
e150
12.0%
t150
12.0%
o100
8.0%
a100
8.0%
i100
8.0%
m100
8.0%
h50
 
4.0%
d50
 
4.0%
Other values (3)150
12.0%
Decimal Number
ValueCountFrequency (%)
966
18.9%
163
18.0%
334
9.7%
234
9.7%
532
9.1%
628
8.0%
427
7.7%
723
 
6.6%
822
 
6.3%
021
 
6.0%
Other Punctuation
ValueCountFrequency (%)
/200
57.1%
.100
28.6%
:50
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin1250
64.1%
Common700
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/200
28.6%
.100
14.3%
966
 
9.4%
163
 
9.0%
:50
 
7.1%
334
 
4.9%
234
 
4.9%
532
 
4.6%
628
 
4.0%
427
 
3.9%
Other values (3)66
 
9.4%
Latin
ValueCountFrequency (%)
p150
12.0%
s150
12.0%
e150
12.0%
t150
12.0%
o100
8.0%
a100
8.0%
i100
8.0%
m100
8.0%
h50
 
4.0%
d50
 
4.0%
Other values (3)150
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/200
 
10.3%
p150
 
7.7%
s150
 
7.7%
e150
 
7.7%
t150
 
7.7%
o100
 
5.1%
a100
 
5.1%
i100
 
5.1%
.100
 
5.1%
m100
 
5.1%
Other values (16)650
33.3%

_embedded.show.id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct38
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46742.66
Minimum10892
Maximum61755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-09-04T23:46:28.692480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10892
5-th percentile18109.6
Q143157.5
median52424
Q352743
95-th percentile59702.15
Maximum61755
Range50863
Interquartile range (IQR)9585.5

Descriptive statistics

Standard deviation12416.46129
Coefficient of variation (CV)0.2656344609
Kurtosis1.839173152
Mean46742.66
Median Absolute Deviation (MAD)1427
Skewness-1.613574112
Sum2337133
Variance154168510.9
MonotonicityNot monotonic
2022-09-04T23:46:28.784559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
526538
 
16.0%
349402
 
4.0%
526852
 
4.0%
527432
 
4.0%
527812
 
4.0%
499482
 
4.0%
108921
 
2.0%
599511
 
2.0%
536691
 
2.0%
586451
 
2.0%
Other values (28)28
56.0%
ValueCountFrequency (%)
108921
2.0%
129061
2.0%
175841
2.0%
187521
2.0%
196281
2.0%
306061
2.0%
334631
2.0%
349402
4.0%
369071
2.0%
393141
2.0%
ValueCountFrequency (%)
617551
2.0%
602461
2.0%
599511
2.0%
593981
2.0%
586451
2.0%
540331
2.0%
536691
2.0%
534671
2.0%
530941
2.0%
528981
2.0%

_embedded.show.url
Categorical

HIGH CORRELATION

Distinct38
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
https://www.tvmaze.com/shows/52653/outlier
https://www.tvmaze.com/shows/34940/fancy-nancy
 
2
https://www.tvmaze.com/shows/52685/the-controllers
 
2
https://www.tvmaze.com/shows/52743/the-penalty-zone
 
2
https://www.tvmaze.com/shows/52781/love-script
 
2
Other values (33)
34 

Length

Max length77
Median length59
Mean length48.16
Min length39

Characters and Unicode

Total characters2408
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)64.0%

Sample

1st rowhttps://www.tvmaze.com/shows/10892/troe-iz-prostokvasino
2nd rowhttps://www.tvmaze.com/shows/19628/top-10-po-versii-seasonvarru
3rd rowhttps://www.tvmaze.com/shows/51471/hero-return
4th rowhttps://www.tvmaze.com/shows/52178/swallowed-star
5th rowhttps://www.tvmaze.com/shows/54033/wu-shen-zhu-zai

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/shows/52653/outlier8
 
16.0%
https://www.tvmaze.com/shows/34940/fancy-nancy2
 
4.0%
https://www.tvmaze.com/shows/52685/the-controllers2
 
4.0%
https://www.tvmaze.com/shows/52743/the-penalty-zone2
 
4.0%
https://www.tvmaze.com/shows/52781/love-script2
 
4.0%
https://www.tvmaze.com/shows/49948/love-revolution2
 
4.0%
https://www.tvmaze.com/shows/10892/troe-iz-prostokvasino1
 
2.0%
https://www.tvmaze.com/shows/59951/awesomeness-tvs-next-influencer1
 
2.0%
https://www.tvmaze.com/shows/53669/lulu1
 
2.0%
https://www.tvmaze.com/shows/58645/the-motive1
 
2.0%
Other values (28)28
56.0%

Length

2022-09-04T23:46:28.880722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/shows/52653/outlier8
 
16.0%
https://www.tvmaze.com/shows/52685/the-controllers2
 
4.0%
https://www.tvmaze.com/shows/52743/the-penalty-zone2
 
4.0%
https://www.tvmaze.com/shows/52781/love-script2
 
4.0%
https://www.tvmaze.com/shows/49948/love-revolution2
 
4.0%
https://www.tvmaze.com/shows/34940/fancy-nancy2
 
4.0%
https://www.tvmaze.com/shows/49524/30-monedas1
 
2.0%
https://www.tvmaze.com/shows/54033/wu-shen-zhu-zai1
 
2.0%
https://www.tvmaze.com/shows/50398/mans-diary1
 
2.0%
https://www.tvmaze.com/shows/52898/legend-of-yun-qian1
 
2.0%
Other values (28)28
56.0%

Most occurring characters

ValueCountFrequency (%)
/250
 
10.4%
w206
 
8.6%
t196
 
8.1%
s185
 
7.7%
o151
 
6.3%
e119
 
4.9%
h117
 
4.9%
m108
 
4.5%
.100
 
4.2%
a94
 
3.9%
Other values (30)882
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1694
70.3%
Other Punctuation400
 
16.6%
Decimal Number255
 
10.6%
Dash Punctuation59
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w206
12.2%
t196
11.6%
s185
10.9%
o151
8.9%
e119
 
7.0%
h117
 
6.9%
m108
 
6.4%
a94
 
5.5%
c75
 
4.4%
p67
 
4.0%
Other values (16)376
22.2%
Decimal Number
ValueCountFrequency (%)
550
19.6%
333
12.9%
228
11.0%
428
11.0%
925
9.8%
624
9.4%
120
 
7.8%
819
 
7.5%
015
 
5.9%
713
 
5.1%
Other Punctuation
ValueCountFrequency (%)
/250
62.5%
.100
 
25.0%
:50
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-59
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1694
70.3%
Common714
29.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
w206
12.2%
t196
11.6%
s185
10.9%
o151
8.9%
e119
 
7.0%
h117
 
6.9%
m108
 
6.4%
a94
 
5.5%
c75
 
4.4%
p67
 
4.0%
Other values (16)376
22.2%
Common
ValueCountFrequency (%)
/250
35.0%
.100
 
14.0%
-59
 
8.3%
:50
 
7.0%
550
 
7.0%
333
 
4.6%
228
 
3.9%
428
 
3.9%
925
 
3.5%
624
 
3.4%
Other values (4)67
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/250
 
10.4%
w206
 
8.6%
t196
 
8.1%
s185
 
7.7%
o151
 
6.3%
e119
 
4.9%
h117
 
4.9%
m108
 
4.5%
.100
 
4.2%
a94
 
3.9%
Other values (30)882
36.6%

_embedded.show.name
Categorical

HIGH CORRELATION

Distinct38
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
Outlier
Fancy Nancy
 
2
The Controllers
 
2
The Penalty Zone
 
2
Love Script
 
2
Other values (33)
34 

Length

Max length43
Median length21
Mean length13.4
Min length5

Characters and Unicode

Total characters670
Distinct characters88
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)64.0%

Sample

1st rowТрое из Простоквашино
2nd rowТОП-10 по версии Seasonvar.ru
3rd rowHero Return
4th rowSwallowed Star
5th rowWu Shen Zhu Zai

Common Values

ValueCountFrequency (%)
Outlier8
 
16.0%
Fancy Nancy2
 
4.0%
The Controllers2
 
4.0%
The Penalty Zone2
 
4.0%
Love Script2
 
4.0%
Love Revolution2
 
4.0%
Трое из Простоквашино1
 
2.0%
Awesomeness TV's Next Influencer1
 
2.0%
Lu'lu'1
 
2.0%
The Motive1
 
2.0%
Other values (28)28
56.0%

Length

2022-09-04T23:46:29.097152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
outlier8
 
7.4%
the6
 
5.6%
love4
 
3.7%
zone2
 
1.9%
fancy2
 
1.9%
script2
 
1.9%
revolution2
 
1.9%
penalty2
 
1.9%
controllers2
 
1.9%
nancy2
 
1.9%
Other values (75)76
70.4%

Most occurring characters

ValueCountFrequency (%)
e63
 
9.4%
58
 
8.7%
n39
 
5.8%
i34
 
5.1%
a34
 
5.1%
r33
 
4.9%
o33
 
4.9%
t32
 
4.8%
l29
 
4.3%
u27
 
4.0%
Other values (78)288
43.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter494
73.7%
Uppercase Letter101
 
15.1%
Space Separator58
 
8.7%
Other Punctuation9
 
1.3%
Decimal Number5
 
0.7%
Open Punctuation1
 
0.1%
Dash Punctuation1
 
0.1%
Close Punctuation1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e63
12.8%
n39
 
7.9%
i34
 
6.9%
a34
 
6.9%
r33
 
6.7%
o33
 
6.7%
t32
 
6.5%
l29
 
5.9%
u27
 
5.5%
s21
 
4.3%
Other values (38)149
30.2%
Uppercase Letter
ValueCountFrequency (%)
O11
 
10.9%
T10
 
9.9%
S9
 
8.9%
L9
 
8.9%
F6
 
5.9%
M5
 
5.0%
P5
 
5.0%
C5
 
5.0%
B4
 
4.0%
Z4
 
4.0%
Other values (19)33
32.7%
Decimal Number
ValueCountFrequency (%)
02
40.0%
31
20.0%
11
20.0%
71
20.0%
Other Punctuation
ValueCountFrequency (%)
'6
66.7%
:2
 
22.2%
.1
 
11.1%
Space Separator
ValueCountFrequency (%)
58
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin533
79.6%
Common75
 
11.2%
Cyrillic62
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e63
 
11.8%
n39
 
7.3%
i34
 
6.4%
a34
 
6.4%
r33
 
6.2%
o33
 
6.2%
t32
 
6.0%
l29
 
5.4%
u27
 
5.1%
s21
 
3.9%
Other values (41)188
35.3%
Cyrillic
ValueCountFrequency (%)
и8
 
12.9%
о6
 
9.7%
е5
 
8.1%
к4
 
6.5%
р4
 
6.5%
с4
 
6.5%
а3
 
4.8%
я2
 
3.2%
Т2
 
3.2%
п2
 
3.2%
Other values (16)22
35.5%
Common
ValueCountFrequency (%)
58
77.3%
'6
 
8.0%
02
 
2.7%
:2
 
2.7%
31
 
1.3%
(1
 
1.3%
.1
 
1.3%
11
 
1.3%
-1
 
1.3%
71
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII604
90.1%
Cyrillic62
 
9.3%
None4
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e63
 
10.4%
58
 
9.6%
n39
 
6.5%
i34
 
5.6%
a34
 
5.6%
r33
 
5.5%
o33
 
5.5%
t32
 
5.3%
l29
 
4.8%
u27
 
4.5%
Other values (50)222
36.8%
Cyrillic
ValueCountFrequency (%)
и8
 
12.9%
о6
 
9.7%
е5
 
8.1%
к4
 
6.5%
р4
 
6.5%
с4
 
6.5%
а3
 
4.8%
я2
 
3.2%
Т2
 
3.2%
п2
 
3.2%
Other values (16)22
35.5%
None
ValueCountFrequency (%)
í2
50.0%
á2
50.0%

_embedded.show.type
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
Scripted
29 
Animation
Talk Show
Documentary
Reality
 
2
Other values (2)

Length

Max length11
Median length8
Mean length8.26
Min length4

Characters and Unicode

Total characters413
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st rowAnimation
2nd rowTalk Show
3rd rowAnimation
4th rowAnimation
5th rowAnimation

Common Values

ValueCountFrequency (%)
Scripted29
58.0%
Animation8
 
16.0%
Talk Show5
 
10.0%
Documentary3
 
6.0%
Reality2
 
4.0%
News2
 
4.0%
Game Show1
 
2.0%

Length

2022-09-04T23:46:29.186152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:46:29.262152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
scripted29
51.8%
animation8
 
14.3%
show6
 
10.7%
talk5
 
8.9%
documentary3
 
5.4%
reality2
 
3.6%
news2
 
3.6%
game1
 
1.8%

Most occurring characters

ValueCountFrequency (%)
i47
11.4%
t42
10.2%
e37
9.0%
S35
 
8.5%
r32
 
7.7%
c32
 
7.7%
p29
 
7.0%
d29
 
7.0%
a19
 
4.6%
n19
 
4.6%
Other values (16)92
22.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter351
85.0%
Uppercase Letter56
 
13.6%
Space Separator6
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i47
13.4%
t42
12.0%
e37
10.5%
r32
9.1%
c32
9.1%
p29
8.3%
d29
8.3%
a19
 
5.4%
n19
 
5.4%
o17
 
4.8%
Other values (8)48
13.7%
Uppercase Letter
ValueCountFrequency (%)
S35
62.5%
A8
 
14.3%
T5
 
8.9%
D3
 
5.4%
R2
 
3.6%
N2
 
3.6%
G1
 
1.8%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin407
98.5%
Common6
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i47
11.5%
t42
10.3%
e37
9.1%
S35
8.6%
r32
 
7.9%
c32
 
7.9%
p29
 
7.1%
d29
 
7.1%
a19
 
4.7%
n19
 
4.7%
Other values (15)86
21.1%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII413
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i47
11.4%
t42
10.2%
e37
9.0%
S35
 
8.5%
r32
 
7.7%
c32
 
7.7%
p29
 
7.0%
d29
 
7.0%
a19
 
4.6%
n19
 
4.6%
Other values (16)92
22.3%

_embedded.show.language
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
Chinese
11 
Norwegian
10 
English
Russian
Korean
Other values (9)
13 

Length

Max length10
Median length7
Mean length7.38
Min length6

Characters and Unicode

Total characters369
Distinct characters30
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)12.0%

Sample

1st rowRussian
2nd rowRussian
3rd rowChinese
4th rowChinese
5th rowChinese

Common Values

ValueCountFrequency (%)
Chinese11
22.0%
Norwegian10
20.0%
English8
16.0%
Russian5
10.0%
Korean3
 
6.0%
Spanish3
 
6.0%
Japanese2
 
4.0%
Arabic2
 
4.0%
Danish1
 
2.0%
Romanian1
 
2.0%
Other values (4)4
 
8.0%

Length

2022-09-04T23:46:29.345151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chinese11
22.0%
norwegian10
20.0%
english8
16.0%
russian5
10.0%
korean3
 
6.0%
spanish3
 
6.0%
japanese2
 
4.0%
arabic2
 
4.0%
danish1
 
2.0%
romanian1
 
2.0%
Other values (4)4
 
8.0%

Most occurring characters

ValueCountFrequency (%)
n45
12.2%
e44
11.9%
i42
11.4%
s37
10.0%
a32
8.7%
h24
 
6.5%
g21
 
5.7%
r17
 
4.6%
o16
 
4.3%
w12
 
3.3%
Other values (20)79
21.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter319
86.4%
Uppercase Letter50
 
13.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n45
14.1%
e44
13.8%
i42
13.2%
s37
11.6%
a32
10.0%
h24
7.5%
g21
6.6%
r17
 
5.3%
o16
 
5.0%
w12
 
3.8%
Other values (8)29
9.1%
Uppercase Letter
ValueCountFrequency (%)
C11
22.0%
N10
20.0%
E8
16.0%
R6
12.0%
S4
 
8.0%
K3
 
6.0%
J2
 
4.0%
A2
 
4.0%
D1
 
2.0%
T1
 
2.0%
Other values (2)2
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Latin369
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n45
12.2%
e44
11.9%
i42
11.4%
s37
10.0%
a32
8.7%
h24
 
6.5%
g21
 
5.7%
r17
 
4.6%
o16
 
4.3%
w12
 
3.3%
Other values (20)79
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII369
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n45
12.2%
e44
11.9%
i42
11.4%
s37
10.0%
a32
8.7%
h24
 
6.5%
g21
 
5.7%
r17
 
4.6%
o16
 
4.3%
w12
 
3.3%
Other values (20)79
21.4%

_embedded.show.genres
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size528.0 B

_embedded.show.status
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
Running
26 
Ended
22 
To Be Determined
 
2

Length

Max length16
Median length7
Mean length6.48
Min length5

Characters and Unicode

Total characters324
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRunning
2nd rowRunning
3rd rowRunning
4th rowRunning
5th rowRunning

Common Values

ValueCountFrequency (%)
Running26
52.0%
Ended22
44.0%
To Be Determined2
 
4.0%

Length

2022-09-04T23:46:29.425151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:46:29.498152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
running26
48.1%
ended22
40.7%
to2
 
3.7%
be2
 
3.7%
determined2
 
3.7%

Most occurring characters

ValueCountFrequency (%)
n102
31.5%
d46
14.2%
e30
 
9.3%
i28
 
8.6%
R26
 
8.0%
u26
 
8.0%
g26
 
8.0%
E22
 
6.8%
4
 
1.2%
T2
 
0.6%
Other values (6)12
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter266
82.1%
Uppercase Letter54
 
16.7%
Space Separator4
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n102
38.3%
d46
17.3%
e30
 
11.3%
i28
 
10.5%
u26
 
9.8%
g26
 
9.8%
o2
 
0.8%
t2
 
0.8%
r2
 
0.8%
m2
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
R26
48.1%
E22
40.7%
T2
 
3.7%
B2
 
3.7%
D2
 
3.7%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin320
98.8%
Common4
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n102
31.9%
d46
14.4%
e30
 
9.4%
i28
 
8.8%
R26
 
8.1%
u26
 
8.1%
g26
 
8.1%
E22
 
6.9%
T2
 
0.6%
o2
 
0.6%
Other values (5)10
 
3.1%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n102
31.5%
d46
14.2%
e30
 
9.3%
i28
 
8.6%
R26
 
8.0%
u26
 
8.0%
g26
 
8.0%
E22
 
6.8%
4
 
1.2%
T2
 
0.6%
Other values (6)12
 
3.7%

_embedded.show.runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct16
Distinct (%)50.0%
Missing18
Missing (%)36.0%
Infinite0
Infinite (%)0.0%
Mean36.25
Minimum4
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-09-04T23:46:29.562154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile7.55
Q115
median35
Q345
95-th percentile87
Maximum120
Range116
Interquartile range (IQR)30

Descriptive statistics

Standard deviation27.92732504
Coefficient of variation (CV)0.7704089666
Kurtosis3.403441749
Mean36.25
Median Absolute Deviation (MAD)15
Skewness1.633091421
Sum1160
Variance779.9354839
MonotonicityNot monotonic
2022-09-04T23:46:29.639156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
458
16.0%
153
 
6.0%
603
 
6.0%
1202
 
4.0%
122
 
4.0%
202
 
4.0%
252
 
4.0%
402
 
4.0%
71
 
2.0%
501
 
2.0%
Other values (6)6
 
12.0%
(Missing)18
36.0%
ValueCountFrequency (%)
41
 
2.0%
71
 
2.0%
81
 
2.0%
91
 
2.0%
111
 
2.0%
122
4.0%
153
6.0%
202
4.0%
221
 
2.0%
252
4.0%
ValueCountFrequency (%)
1202
 
4.0%
603
 
6.0%
501
 
2.0%
458
16.0%
402
 
4.0%
301
 
2.0%
252
 
4.0%
221
 
2.0%
202
 
4.0%
153
 
6.0%

_embedded.show.averageRuntime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)57.4%
Missing3
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean37.95744681
Minimum4
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-09-04T23:46:29.717151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9
Q120
median43
Q345
95-th percentile65.4
Maximum120
Range116
Interquartile range (IQR)25

Descriptive statistics

Standard deviation24.12553892
Coefficient of variation (CV)0.6355943549
Kurtosis4.021709382
Mean37.95744681
Median Absolute Deviation (MAD)15
Skewness1.530174157
Sum1784
Variance582.0416281
MonotonicityNot monotonic
2022-09-04T23:46:29.805230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
439
18.0%
457
14.0%
1202
 
4.0%
92
 
4.0%
202
 
4.0%
602
 
4.0%
122
 
4.0%
152
 
4.0%
591
 
2.0%
251
 
2.0%
Other values (17)17
34.0%
(Missing)3
 
6.0%
ValueCountFrequency (%)
41
2.0%
81
2.0%
92
4.0%
122
4.0%
131
2.0%
141
2.0%
152
4.0%
161
2.0%
202
4.0%
211
2.0%
ValueCountFrequency (%)
1202
 
4.0%
661
 
2.0%
641
 
2.0%
602
 
4.0%
591
 
2.0%
561
 
2.0%
471
 
2.0%
457
14.0%
439
18.0%
401
 
2.0%

_embedded.show.premiered
Categorical

HIGH CORRELATION

Distinct31
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
2020-12-27
12 
2020-11-22
2020-11-29
 
2
2018-07-13
 
2
2020-09-01
 
2
Other values (26)
29 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters500
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)46.0%

Sample

1st row1978-06-10
2nd row2015-11-27
3rd row2020-10-18
4th row2020-11-29
5th row2020-03-08

Common Values

ValueCountFrequency (%)
2020-12-2712
24.0%
2020-11-223
 
6.0%
2020-11-292
 
4.0%
2018-07-132
 
4.0%
2020-09-012
 
4.0%
2020-12-202
 
4.0%
2020-12-162
 
4.0%
2020-12-262
 
4.0%
2019-07-291
 
2.0%
2017-11-021
 
2.0%
Other values (21)21
42.0%

Length

2022-09-04T23:46:29.888236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-2712
24.0%
2020-11-223
 
6.0%
2020-11-292
 
4.0%
2018-07-132
 
4.0%
2020-09-012
 
4.0%
2020-12-202
 
4.0%
2020-12-162
 
4.0%
2020-12-262
 
4.0%
2020-11-081
 
2.0%
2018-10-241
 
2.0%
Other values (21)21
42.0%

Most occurring characters

ValueCountFrequency (%)
2140
28.0%
0116
23.2%
-100
20.0%
175
15.0%
723
 
4.6%
913
 
2.6%
89
 
1.8%
68
 
1.6%
36
 
1.2%
55
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number400
80.0%
Dash Punctuation100
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2140
35.0%
0116
29.0%
175
18.8%
723
 
5.8%
913
 
3.2%
89
 
2.2%
68
 
2.0%
36
 
1.5%
55
 
1.2%
45
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
-100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2140
28.0%
0116
23.2%
-100
20.0%
175
15.0%
723
 
4.6%
913
 
2.6%
89
 
1.8%
68
 
1.6%
36
 
1.2%
55
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2140
28.0%
0116
23.2%
-100
20.0%
175
15.0%
723
 
4.6%
913
 
2.6%
89
 
1.8%
68
 
1.6%
36
 
1.2%
55
 
1.0%

_embedded.show.ended
Categorical

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)45.5%
Missing28
Missing (%)56.0%
Memory size528.0 B
2020-12-27
10 
2021-01-19
2021-01-09
2021-01-25
2020-12-31
 
1
Other values (5)

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters220
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)27.3%

Sample

1st row2020-12-31
2nd row2020-12-27
3rd row2020-12-27
4th row2021-01-17
5th row2021-01-03

Common Values

ValueCountFrequency (%)
2020-12-2710
 
20.0%
2021-01-192
 
4.0%
2021-01-092
 
4.0%
2021-01-252
 
4.0%
2020-12-311
 
2.0%
2021-01-171
 
2.0%
2021-01-031
 
2.0%
2021-03-011
 
2.0%
2021-01-311
 
2.0%
2020-12-301
 
2.0%
(Missing)28
56.0%

Length

2022-09-04T23:46:29.959230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:46:30.049379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-2710
45.5%
2021-01-192
 
9.1%
2021-01-092
 
9.1%
2021-01-252
 
9.1%
2020-12-311
 
4.5%
2021-01-171
 
4.5%
2021-01-031
 
4.5%
2021-03-011
 
4.5%
2021-01-311
 
4.5%
2020-12-301
 
4.5%

Most occurring characters

ValueCountFrequency (%)
268
30.9%
049
22.3%
-44
20.0%
137
16.8%
711
 
5.0%
35
 
2.3%
94
 
1.8%
52
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number176
80.0%
Dash Punctuation44
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
268
38.6%
049
27.8%
137
21.0%
711
 
6.2%
35
 
2.8%
94
 
2.3%
52
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
-44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common220
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
268
30.9%
049
22.3%
-44
20.0%
137
16.8%
711
 
5.0%
35
 
2.3%
94
 
1.8%
52
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII220
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
268
30.9%
049
22.3%
-44
20.0%
137
16.8%
711
 
5.0%
35
 
2.3%
94
 
1.8%
52
 
0.9%

_embedded.show.officialSite
Categorical

HIGH CORRELATION
MISSING

Distinct35
Distinct (%)77.8%
Missing5
Missing (%)10.0%
Memory size528.0 B
https://hbonordic.com/series/outlier/b6fcd668-52c9-4200-a80f-e1fb829ebe7d
https://disneynow.com/shows/fancy-nancy
 
2
https://www.iqiyi.com/a_19rrhllpip.html
 
2
https://tv.kakao.com/channel/3643849/cliplink/412069527?metaObjectType=Channel
 
2
https://okko.tv/serial/prostokvashino
 
1
Other values (30)
30 

Length

Max length85
Median length72
Mean length54.68888889
Min length28

Characters and Unicode

Total characters2461
Distinct characters67
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)68.9%

Sample

1st rowhttps://okko.tv/serial/prostokvashino
2nd rowhttp://seasonvar.ru/serial-12772-TOP-10_po_versii_Seasonvarru-1-season.html
3rd rowhttps://v.qq.com/detail/q/q72jd29a3oxflsr.html
4th rowhttps://v.qq.com/detail/3/324olz7ilvo2j5f.html
5th rowhttps://v.qq.com/detail/m/7q544xyrava3vxf.html

Common Values

ValueCountFrequency (%)
https://hbonordic.com/series/outlier/b6fcd668-52c9-4200-a80f-e1fb829ebe7d8
 
16.0%
https://disneynow.com/shows/fancy-nancy2
 
4.0%
https://www.iqiyi.com/a_19rrhllpip.html2
 
4.0%
https://tv.kakao.com/channel/3643849/cliplink/412069527?metaObjectType=Channel2
 
4.0%
https://okko.tv/serial/prostokvashino1
 
2.0%
https://pro-tv.info/projects/dekonstruktsiya/1
 
2.0%
https://www.discoveryplus.se/program/pappas-pojkar1
 
2.0%
https://shahid.mbc.net/en/series/Lu'lu'/series-8256961
 
2.0%
https://www.netflix.com/title/814497541
 
2.0%
https://www.paramountplus.com/shows/awesomeness-tvs-next-influencer/1
 
2.0%
Other values (25)25
50.0%
(Missing)5
 
10.0%

Length

2022-09-04T23:46:30.159533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://hbonordic.com/series/outlier/b6fcd668-52c9-4200-a80f-e1fb829ebe7d8
 
17.8%
https://www.iqiyi.com/a_19rrhllpip.html2
 
4.4%
https://tv.kakao.com/channel/3643849/cliplink/412069527?metaobjecttype=channel2
 
4.4%
https://disneynow.com/shows/fancy-nancy2
 
4.4%
https://www.atresplayer.com/series/byanamilan1
 
2.2%
https://www.youtube.com/channel/uc1efxmjnkjitxpfwty6rswg1
 
2.2%
https://viaplay.dk/serier/friheden1
 
2.2%
https://www.youtube.com/user/scishow1
 
2.2%
https://tv.nrk.no/serie/labyrint1
 
2.2%
https://www.atresplayer.com/series/fisica-o-quimica-el-reencuentro1
 
2.2%
Other values (25)25
55.6%

Most occurring characters

ValueCountFrequency (%)
/208
 
8.5%
e168
 
6.8%
t165
 
6.7%
s136
 
5.5%
o121
 
4.9%
i98
 
4.0%
h97
 
3.9%
c96
 
3.9%
a92
 
3.7%
n84
 
3.4%
Other values (57)1196
48.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1704
69.2%
Other Punctuation336
 
13.7%
Decimal Number289
 
11.7%
Dash Punctuation80
 
3.3%
Uppercase Letter44
 
1.8%
Connector Punctuation6
 
0.2%
Math Symbol2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e168
 
9.9%
t165
 
9.7%
s136
 
8.0%
o121
 
7.1%
i98
 
5.8%
h97
 
5.7%
c96
 
5.6%
a92
 
5.4%
n84
 
4.9%
r83
 
4.9%
Other values (16)564
33.1%
Uppercase Letter
ValueCountFrequency (%)
T5
 
11.4%
C5
 
11.4%
O4
 
9.1%
F3
 
6.8%
U3
 
6.8%
A2
 
4.5%
W2
 
4.5%
B2
 
4.5%
Y2
 
4.5%
Z2
 
4.5%
Other values (12)14
31.8%
Decimal Number
ValueCountFrequency (%)
245
15.6%
037
12.8%
637
12.8%
831
10.7%
928
9.7%
428
9.7%
724
8.3%
123
8.0%
520
6.9%
316
 
5.5%
Other Punctuation
ValueCountFrequency (%)
/208
61.9%
.78
 
23.2%
:45
 
13.4%
?2
 
0.6%
'2
 
0.6%
%1
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
-80
100.0%
Connector Punctuation
ValueCountFrequency (%)
_6
100.0%
Math Symbol
ValueCountFrequency (%)
=2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1748
71.0%
Common713
29.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e168
 
9.6%
t165
 
9.4%
s136
 
7.8%
o121
 
6.9%
i98
 
5.6%
h97
 
5.5%
c96
 
5.5%
a92
 
5.3%
n84
 
4.8%
r83
 
4.7%
Other values (38)608
34.8%
Common
ValueCountFrequency (%)
/208
29.2%
-80
 
11.2%
.78
 
10.9%
245
 
6.3%
:45
 
6.3%
037
 
5.2%
637
 
5.2%
831
 
4.3%
928
 
3.9%
428
 
3.9%
Other values (9)96
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2461
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/208
 
8.5%
e168
 
6.8%
t165
 
6.7%
s136
 
5.5%
o121
 
4.9%
i98
 
4.0%
h97
 
3.9%
c96
 
3.9%
a92
 
3.7%
n84
 
3.4%
Other values (57)1196
48.6%

_embedded.show.schedule.time
Categorical

HIGH CORRELATION

Distinct8
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
34 
20:00
12:00
 
3
10:00
 
3
17:00
 
2
Other values (3)
 
3

Length

Max length5
Median length0
Mean length1.6
Min length0

Characters and Unicode

Total characters80
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)6.0%

Sample

1st row12:00
2nd row
3rd row10:00
4th row10:00
5th row10:00

Common Values

ValueCountFrequency (%)
34
68.0%
20:005
 
10.0%
12:003
 
6.0%
10:003
 
6.0%
17:002
 
4.0%
19:001
 
2.0%
18:001
 
2.0%
22:301
 
2.0%

Length

2022-09-04T23:46:30.247718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:46:30.334718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
20:005
31.2%
12:003
18.8%
10:003
18.8%
17:002
 
12.5%
19:001
 
6.2%
18:001
 
6.2%
22:301
 
6.2%

Most occurring characters

ValueCountFrequency (%)
039
48.8%
:16
20.0%
210
 
12.5%
110
 
12.5%
72
 
2.5%
91
 
1.2%
81
 
1.2%
31
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number64
80.0%
Other Punctuation16
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
039
60.9%
210
 
15.6%
110
 
15.6%
72
 
3.1%
91
 
1.6%
81
 
1.6%
31
 
1.6%
Other Punctuation
ValueCountFrequency (%)
:16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common80
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
039
48.8%
:16
20.0%
210
 
12.5%
110
 
12.5%
72
 
2.5%
91
 
1.2%
81
 
1.2%
31
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII80
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
039
48.8%
:16
20.0%
210
 
12.5%
110
 
12.5%
72
 
2.5%
91
 
1.2%
81
 
1.2%
31
 
1.2%

_embedded.show.schedule.days
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size528.0 B

_embedded.show.rating.average
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)36.4%
Missing39
Missing (%)78.0%
Memory size528.0 B
5.2
7.5
7.7
8.1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters33
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)27.3%

Sample

1st row7.5
2nd row7.7
3rd row8.1
4th row5.2
5th row5.2

Common Values

ValueCountFrequency (%)
5.28
 
16.0%
7.51
 
2.0%
7.71
 
2.0%
8.11
 
2.0%
(Missing)39
78.0%

Length

2022-09-04T23:46:30.415518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:46:30.494629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
5.28
72.7%
7.51
 
9.1%
7.71
 
9.1%
8.11
 
9.1%

Most occurring characters

ValueCountFrequency (%)
.11
33.3%
59
27.3%
28
24.2%
73
 
9.1%
81
 
3.0%
11
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number22
66.7%
Other Punctuation11
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
59
40.9%
28
36.4%
73
 
13.6%
81
 
4.5%
11
 
4.5%
Other Punctuation
ValueCountFrequency (%)
.11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common33
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.11
33.3%
59
27.3%
28
24.2%
73
 
9.1%
81
 
3.0%
11
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII33
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.11
33.3%
59
27.3%
28
24.2%
73
 
9.1%
81
 
3.0%
11
 
3.0%

_embedded.show.weight
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct32
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.2
Minimum2
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-09-04T23:46:30.575733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.35
Q120.5
median34
Q355
95-th percentile84.75
Maximum93
Range91
Interquartile range (IQR)34.5

Descriptive statistics

Standard deviation23.53547041
Coefficient of variation (CV)0.6003946533
Kurtosis-0.3038696953
Mean39.2
Median Absolute Deviation (MAD)16
Skewness0.6179303569
Sum1960
Variance553.9183673
MonotonicityNot monotonic
2022-09-04T23:46:30.659853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
558
 
16.0%
206
 
12.0%
353
 
6.0%
822
 
4.0%
282
 
4.0%
602
 
4.0%
242
 
4.0%
891
 
2.0%
181
 
2.0%
271
 
2.0%
Other values (22)22
44.0%
ValueCountFrequency (%)
21
 
2.0%
31
 
2.0%
41
 
2.0%
71
 
2.0%
121
 
2.0%
161
 
2.0%
181
 
2.0%
206
12.0%
221
 
2.0%
231
 
2.0%
ValueCountFrequency (%)
931
 
2.0%
891
 
2.0%
871
 
2.0%
822
 
4.0%
791
 
2.0%
602
 
4.0%
561
 
2.0%
558
16.0%
511
 
2.0%
501
 
2.0%

_embedded.show.network
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50
Missing (%)100.0%
Memory size528.0 B

_embedded.show.webChannel.id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct26
Distinct (%)54.2%
Missing2
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean190.875
Minimum1
Maximum445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-09-04T23:46:30.741853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21
Q164.25
median199.5
Q3330
95-th percentile378.3
Maximum445
Range444
Interquartile range (IQR)265.75

Descriptive statistics

Standard deviation137.7638474
Coefficient of variation (CV)0.721749037
Kurtosis-1.574736035
Mean190.875
Median Absolute Deviation (MAD)130.5
Skewness0.1085210967
Sum9162
Variance18978.87766
MonotonicityNot monotonic
2022-09-04T23:46:30.819852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
3308
16.0%
216
 
12.0%
2264
 
8.0%
1043
 
6.0%
672
 
4.0%
3792
 
4.0%
3772
 
4.0%
2942
 
4.0%
832
 
4.0%
1021
 
2.0%
Other values (16)16
32.0%
(Missing)2
 
4.0%
ValueCountFrequency (%)
11
 
2.0%
216
12.0%
221
 
2.0%
301
 
2.0%
321
 
2.0%
511
 
2.0%
561
 
2.0%
672
 
4.0%
832
 
4.0%
1021
 
2.0%
ValueCountFrequency (%)
4451
 
2.0%
3792
 
4.0%
3772
 
4.0%
3661
 
2.0%
3311
 
2.0%
3308
16.0%
3271
 
2.0%
2942
 
4.0%
2651
 
2.0%
2381
 
2.0%

_embedded.show.webChannel.name
Categorical

HIGH CORRELATION
MISSING

Distinct26
Distinct (%)54.2%
Missing2
Missing (%)4.0%
Memory size528.0 B
HBO Nordic
YouTube
Mango TV
Tencent QQ
iQIYI
 
2
Other values (21)
25 

Length

Max length19
Median length12
Mean length8.479166667
Min length4

Characters and Unicode

Total characters407
Distinct characters48
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)35.4%

Sample

1st rowOkko
2nd rowSeasonvar
3rd rowTencent QQ
4th rowTencent QQ
5th rowTencent QQ

Common Values

ValueCountFrequency (%)
HBO Nordic8
16.0%
YouTube6
 
12.0%
Mango TV4
 
8.0%
Tencent QQ3
 
6.0%
iQIYI2
 
4.0%
Shahid2
 
4.0%
ATRESplayer PREMIUM2
 
4.0%
Kakao TV2
 
4.0%
DisneyNOW2
 
4.0%
Twit1
 
2.0%
Other values (16)16
32.0%
(Missing)2
 
4.0%

Length

2022-09-04T23:46:30.906852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hbo9
 
12.2%
tv9
 
12.2%
nordic8
 
10.8%
youtube6
 
8.1%
mango4
 
5.4%
tencent3
 
4.1%
qq3
 
4.1%
premium2
 
2.7%
disneynow2
 
2.7%
kakao2
 
2.7%
Other values (23)26
35.1%

Most occurring characters

ValueCountFrequency (%)
o29
 
7.1%
26
 
6.4%
e24
 
5.9%
T24
 
5.9%
a22
 
5.4%
i22
 
5.4%
r15
 
3.7%
N14
 
3.4%
u14
 
3.4%
n14
 
3.4%
Other values (38)203
49.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter228
56.0%
Uppercase Letter149
36.6%
Space Separator26
 
6.4%
Math Symbol3
 
0.7%
Decimal Number1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o29
12.7%
e24
10.5%
a22
 
9.6%
i22
 
9.6%
r15
 
6.6%
u14
 
6.1%
n14
 
6.1%
c12
 
5.3%
d11
 
4.8%
t9
 
3.9%
Other values (13)56
24.6%
Uppercase Letter
ValueCountFrequency (%)
T24
16.1%
N14
 
9.4%
O12
 
8.1%
V11
 
7.4%
B10
 
6.7%
H9
 
6.0%
M8
 
5.4%
Q8
 
5.4%
Y8
 
5.4%
I7
 
4.7%
Other values (12)38
25.5%
Space Separator
ValueCountFrequency (%)
26
100.0%
Math Symbol
ValueCountFrequency (%)
+3
100.0%
Decimal Number
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin377
92.6%
Common30
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o29
 
7.7%
e24
 
6.4%
T24
 
6.4%
a22
 
5.8%
i22
 
5.8%
r15
 
4.0%
N14
 
3.7%
u14
 
3.7%
n14
 
3.7%
O12
 
3.2%
Other values (35)187
49.6%
Common
ValueCountFrequency (%)
26
86.7%
+3
 
10.0%
21
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII407
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o29
 
7.1%
26
 
6.4%
e24
 
5.9%
T24
 
5.9%
a22
 
5.4%
i22
 
5.4%
r15
 
3.7%
N14
 
3.4%
u14
 
3.4%
n14
 
3.4%
Other values (38)203
49.9%

_embedded.show.webChannel.country.name
Categorical

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)32.0%
Missing25
Missing (%)50.0%
Memory size528.0 B
China
United States
Korea, Republic of
Russian Federation
Norway
Other values (3)

Length

Max length25
Median length18
Mean length10.44
Min length5

Characters and Unicode

Total characters261
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)8.0%

Sample

1st rowRussian Federation
2nd rowRussian Federation
3rd rowChina
4th rowChina
5th rowChina

Common Values

ValueCountFrequency (%)
China8
 
16.0%
United States6
 
12.0%
Korea, Republic of3
 
6.0%
Russian Federation2
 
4.0%
Norway2
 
4.0%
Spain2
 
4.0%
Taiwan, Province of China1
 
2.0%
Brazil1
 
2.0%
(Missing)25
50.0%

Length

2022-09-04T23:46:30.995990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:46:31.088990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
china9
21.4%
united6
14.3%
states6
14.3%
of4
9.5%
korea3
 
7.1%
republic3
 
7.1%
russian2
 
4.8%
federation2
 
4.8%
norway2
 
4.8%
spain2
 
4.8%
Other values (3)3
 
7.1%

Most occurring characters

ValueCountFrequency (%)
a29
 
11.1%
i27
 
10.3%
n23
 
8.8%
e23
 
8.8%
t20
 
7.7%
17
 
6.5%
o12
 
4.6%
s10
 
3.8%
C9
 
3.4%
h9
 
3.4%
Other values (22)82
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter202
77.4%
Uppercase Letter38
 
14.6%
Space Separator17
 
6.5%
Other Punctuation4
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a29
14.4%
i27
13.4%
n23
11.4%
e23
11.4%
t20
9.9%
o12
 
5.9%
s10
 
5.0%
h9
 
4.5%
r9
 
4.5%
d8
 
4.0%
Other values (10)32
15.8%
Uppercase Letter
ValueCountFrequency (%)
C9
23.7%
S8
21.1%
U6
15.8%
R5
13.2%
K3
 
7.9%
F2
 
5.3%
N2
 
5.3%
T1
 
2.6%
P1
 
2.6%
B1
 
2.6%
Space Separator
ValueCountFrequency (%)
17
100.0%
Other Punctuation
ValueCountFrequency (%)
,4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin240
92.0%
Common21
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a29
12.1%
i27
 
11.2%
n23
 
9.6%
e23
 
9.6%
t20
 
8.3%
o12
 
5.0%
s10
 
4.2%
C9
 
3.8%
h9
 
3.8%
r9
 
3.8%
Other values (20)69
28.7%
Common
ValueCountFrequency (%)
17
81.0%
,4
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII261
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a29
 
11.1%
i27
 
10.3%
n23
 
8.8%
e23
 
8.8%
t20
 
7.7%
17
 
6.5%
o12
 
4.6%
s10
 
3.8%
C9
 
3.4%
h9
 
3.4%
Other values (22)82
31.4%

_embedded.show.webChannel.country.code
Categorical

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)32.0%
Missing25
Missing (%)50.0%
Memory size528.0 B
CN
US
KR
RU
NO
Other values (3)

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters50
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)8.0%

Sample

1st rowRU
2nd rowRU
3rd rowCN
4th rowCN
5th rowCN

Common Values

ValueCountFrequency (%)
CN8
 
16.0%
US6
 
12.0%
KR3
 
6.0%
RU2
 
4.0%
NO2
 
4.0%
ES2
 
4.0%
TW1
 
2.0%
BR1
 
2.0%
(Missing)25
50.0%

Length

2022-09-04T23:46:31.302989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:46:31.385989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
cn8
32.0%
us6
24.0%
kr3
 
12.0%
ru2
 
8.0%
no2
 
8.0%
es2
 
8.0%
tw1
 
4.0%
br1
 
4.0%

Most occurring characters

ValueCountFrequency (%)
N10
20.0%
C8
16.0%
U8
16.0%
S8
16.0%
R6
12.0%
K3
 
6.0%
O2
 
4.0%
E2
 
4.0%
T1
 
2.0%
W1
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter50
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N10
20.0%
C8
16.0%
U8
16.0%
S8
16.0%
R6
12.0%
K3
 
6.0%
O2
 
4.0%
E2
 
4.0%
T1
 
2.0%
W1
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Latin50
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N10
20.0%
C8
16.0%
U8
16.0%
S8
16.0%
R6
12.0%
K3
 
6.0%
O2
 
4.0%
E2
 
4.0%
T1
 
2.0%
W1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII50
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N10
20.0%
C8
16.0%
U8
16.0%
S8
16.0%
R6
12.0%
K3
 
6.0%
O2
 
4.0%
E2
 
4.0%
T1
 
2.0%
W1
 
2.0%

_embedded.show.webChannel.country.timezone
Categorical

HIGH CORRELATION
MISSING

Distinct8
Distinct (%)32.0%
Missing25
Missing (%)50.0%
Memory size528.0 B
Asia/Shanghai
America/New_York
Asia/Seoul
Asia/Kamchatka
Europe/Oslo
Other values (3)

Length

Max length16
Median length15
Mean length13.28
Min length10

Characters and Unicode

Total characters332
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)8.0%

Sample

1st rowAsia/Kamchatka
2nd rowAsia/Kamchatka
3rd rowAsia/Shanghai
4th rowAsia/Shanghai
5th rowAsia/Shanghai

Common Values

ValueCountFrequency (%)
Asia/Shanghai8
 
16.0%
America/New_York6
 
12.0%
Asia/Seoul3
 
6.0%
Asia/Kamchatka2
 
4.0%
Europe/Oslo2
 
4.0%
Europe/Madrid2
 
4.0%
Asia/Taipei1
 
2.0%
America/Noronha1
 
2.0%
(Missing)25
50.0%

Length

2022-09-04T23:46:31.470989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:46:31.559989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
asia/shanghai8
32.0%
america/new_york6
24.0%
asia/seoul3
 
12.0%
asia/kamchatka2
 
8.0%
europe/oslo2
 
8.0%
europe/madrid2
 
8.0%
asia/taipei1
 
4.0%
america/noronha1
 
4.0%

Most occurring characters

ValueCountFrequency (%)
a47
14.2%
i33
 
9.9%
/25
 
7.5%
A21
 
6.3%
e21
 
6.3%
r20
 
6.0%
h19
 
5.7%
o17
 
5.1%
s16
 
4.8%
S11
 
3.3%
Other values (19)102
30.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter245
73.8%
Uppercase Letter56
 
16.9%
Other Punctuation25
 
7.5%
Connector Punctuation6
 
1.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a47
19.2%
i33
13.5%
e21
8.6%
r20
8.2%
h19
7.8%
o17
 
6.9%
s16
 
6.5%
n9
 
3.7%
m9
 
3.7%
c9
 
3.7%
Other values (8)45
18.4%
Uppercase Letter
ValueCountFrequency (%)
A21
37.5%
S11
19.6%
N7
 
12.5%
Y6
 
10.7%
E4
 
7.1%
K2
 
3.6%
O2
 
3.6%
M2
 
3.6%
T1
 
1.8%
Other Punctuation
ValueCountFrequency (%)
/25
100.0%
Connector Punctuation
ValueCountFrequency (%)
_6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin301
90.7%
Common31
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a47
15.6%
i33
 
11.0%
A21
 
7.0%
e21
 
7.0%
r20
 
6.6%
h19
 
6.3%
o17
 
5.6%
s16
 
5.3%
S11
 
3.7%
n9
 
3.0%
Other values (17)87
28.9%
Common
ValueCountFrequency (%)
/25
80.6%
_6
 
19.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII332
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a47
14.2%
i33
 
9.9%
/25
 
7.5%
A21
 
6.3%
e21
 
6.3%
r20
 
6.0%
h19
 
5.7%
o17
 
5.1%
s16
 
4.8%
S11
 
3.3%
Other values (19)102
30.7%

_embedded.show.webChannel.officialSite
Categorical

HIGH CORRELATION
MISSING

Distinct10
Distinct (%)45.5%
Missing28
Missing (%)56.0%
Memory size528.0 B
https://www.youtube.com
https://w.mgtv.com/
https://v.qq.com/
https://tv.kakao.com/top
https://www.iq.com/
Other values (5)

Length

Max length30
Median length24
Mean length21.59090909
Min length17

Characters and Unicode

Total characters475
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)22.7%

Sample

1st rowhttps://v.qq.com/
2nd rowhttps://v.qq.com/
3rd rowhttps://v.qq.com/
4th rowhttps://tv.kakao.com/top
5th rowhttps://tv.kakao.com/top

Common Values

ValueCountFrequency (%)
https://www.youtube.com6
 
12.0%
https://w.mgtv.com/4
 
8.0%
https://v.qq.com/3
 
6.0%
https://tv.kakao.com/top2
 
4.0%
https://www.iq.com/2
 
4.0%
https://tv.naver.com/1
 
2.0%
https://viaplay.com1
 
2.0%
https://www.discoveryplus.com/1
 
2.0%
https://www.netflix.com/1
 
2.0%
https://www.paramountplus.com/1
 
2.0%
(Missing)28
56.0%

Length

2022-09-04T23:46:31.653336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:46:31.753870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
https://www.youtube.com6
27.3%
https://w.mgtv.com4
18.2%
https://v.qq.com3
13.6%
https://tv.kakao.com/top2
 
9.1%
https://www.iq.com2
 
9.1%
https://tv.naver.com1
 
4.5%
https://viaplay.com1
 
4.5%
https://www.discoveryplus.com1
 
4.5%
https://www.netflix.com1
 
4.5%
https://www.paramountplus.com1
 
4.5%

Most occurring characters

ValueCountFrequency (%)
t61
12.8%
/59
12.4%
.43
 
9.1%
w37
 
7.8%
o34
 
7.2%
p28
 
5.9%
m27
 
5.7%
s25
 
5.3%
c23
 
4.8%
h22
 
4.6%
Other values (17)116
24.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter351
73.9%
Other Punctuation124
 
26.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t61
17.4%
w37
10.5%
o34
9.7%
p28
8.0%
m27
7.7%
s25
 
7.1%
c23
 
6.6%
h22
 
6.3%
u15
 
4.3%
v13
 
3.7%
Other values (14)66
18.8%
Other Punctuation
ValueCountFrequency (%)
/59
47.6%
.43
34.7%
:22
 
17.7%

Most occurring scripts

ValueCountFrequency (%)
Latin351
73.9%
Common124
 
26.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t61
17.4%
w37
10.5%
o34
9.7%
p28
8.0%
m27
7.7%
s25
 
7.1%
c23
 
6.6%
h22
 
6.3%
u15
 
4.3%
v13
 
3.7%
Other values (14)66
18.8%
Common
ValueCountFrequency (%)
/59
47.6%
.43
34.7%
:22
 
17.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII475
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t61
12.8%
/59
12.4%
.43
 
9.1%
w37
 
7.8%
o34
 
7.2%
p28
 
5.9%
m27
 
5.7%
s25
 
5.3%
c23
 
4.8%
h22
 
4.6%
Other values (17)116
24.4%

_embedded.show.dvdCountry
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50
Missing (%)100.0%
Memory size528.0 B

_embedded.show.externals.tvrage
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50
Missing (%)100.0%
Memory size528.0 B

_embedded.show.externals.thetvdb
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct27
Distinct (%)73.0%
Missing13
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean362757.7568
Minimum144991
Maximum395145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-09-04T23:46:31.863870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum144991
5-th percentile263267.2
Q1354458
median390130
Q3393630
95-th percentile393892.6
Maximum395145
Range250154
Interquartile range (IQR)39172

Descriptive statistics

Standard deviation53705.79108
Coefficient of variation (CV)0.148048636
Kurtosis6.87568706
Mean362757.7568
Median Absolute Deviation (MAD)4019
Skewness-2.467952494
Sum13422037
Variance2884311995
MonotonicityNot monotonic
2022-09-04T23:46:31.949870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
3936308
16.0%
3488202
 
4.0%
3861112
 
4.0%
3937262
 
4.0%
2555641
 
2.0%
3919761
 
2.0%
3628631
 
2.0%
3373361
 
2.0%
3103361
 
2.0%
1449911
 
2.0%
Other values (17)17
34.0%
(Missing)13
26.0%
ValueCountFrequency (%)
1449911
2.0%
2555641
2.0%
2651931
2.0%
2776911
2.0%
2941791
2.0%
3103361
2.0%
3373361
2.0%
3488202
4.0%
3544581
2.0%
3628631
2.0%
ValueCountFrequency (%)
3951451
 
2.0%
3940871
 
2.0%
3938441
 
2.0%
3937262
 
4.0%
3936308
16.0%
3925981
 
2.0%
3919761
 
2.0%
3905861
 
2.0%
3904711
 
2.0%
3903421
 
2.0%

_embedded.show.externals.imdb
Categorical

HIGH CORRELATION
MISSING

Distinct18
Distinct (%)64.3%
Missing22
Missing (%)44.0%
Memory size528.0 B
tt13470388
tt2229129
tt13599000
tt12199200
tt10701038
 
1
Other values (13)
13 

Length

Max length10
Median length10
Mean length9.714285714
Min length9

Characters and Unicode

Total characters272
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)50.0%

Sample

1st rowtt6940730
2nd rowtt9764386
3rd rowtt12795100
4th rowtt7694874
5th rowtt10701038

Common Values

ValueCountFrequency (%)
tt134703888
 
16.0%
tt22291292
 
4.0%
tt135990002
 
4.0%
tt121992002
 
4.0%
tt107010381
 
2.0%
tt133394301
 
2.0%
tt130109121
 
2.0%
tt76948741
 
2.0%
tt69407301
 
2.0%
tt127951001
 
2.0%
Other values (8)8
 
16.0%
(Missing)22
44.0%

Length

2022-09-04T23:46:32.038949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tt134703888
28.6%
tt135990002
 
7.1%
tt121992002
 
7.1%
tt22291292
 
7.1%
tt124579461
 
3.6%
tt102418121
 
3.6%
tt97643861
 
3.6%
tt64686941
 
3.6%
tt35416561
 
3.6%
tt03817531
 
3.6%
Other values (8)8
28.6%

Most occurring characters

ValueCountFrequency (%)
t56
20.6%
132
11.8%
031
11.4%
330
11.0%
923
8.5%
822
 
8.1%
420
 
7.4%
218
 
6.6%
716
 
5.9%
614
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number216
79.4%
Lowercase Letter56
 
20.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
132
14.8%
031
14.4%
330
13.9%
923
10.6%
822
10.2%
420
9.3%
218
8.3%
716
7.4%
614
6.5%
510
 
4.6%
Lowercase Letter
ValueCountFrequency (%)
t56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common216
79.4%
Latin56
 
20.6%

Most frequent character per script

Common
ValueCountFrequency (%)
132
14.8%
031
14.4%
330
13.9%
923
10.6%
822
10.2%
420
9.3%
218
8.3%
716
7.4%
614
6.5%
510
 
4.6%
Latin
ValueCountFrequency (%)
t56
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t56
20.6%
132
11.8%
031
11.4%
330
11.0%
923
8.5%
822
 
8.1%
420
 
7.4%
218
 
6.6%
716
 
5.9%
614
 
5.1%

_embedded.show.image.medium
Categorical

HIGH CORRELATION
MISSING

Distinct37
Distinct (%)75.5%
Missing1
Missing (%)2.0%
Memory size528.0 B
https://static.tvmaze.com/uploads/images/medium_portrait/290/727350.jpg
https://static.tvmaze.com/uploads/images/medium_portrait/161/402691.jpg
 
2
https://static.tvmaze.com/uploads/images/medium_portrait/291/727817.jpg
 
2
https://static.tvmaze.com/uploads/images/medium_portrait/291/729147.jpg
 
2
https://static.tvmaze.com/uploads/images/medium_portrait/291/729461.jpg
 
2
Other values (32)
33 

Length

Max length72
Median length71
Mean length70.95918367
Min length70

Characters and Unicode

Total characters3477
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)63.3%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/medium_portrait/51/128137.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/medium_portrait/69/173595.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/medium_portrait/279/698895.jpg
4th rowhttps://static.tvmaze.com/uploads/images/medium_portrait/286/715165.jpg
5th rowhttps://static.tvmaze.com/uploads/images/medium_portrait/299/748854.jpg

Common Values

ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_portrait/290/727350.jpg8
 
16.0%
https://static.tvmaze.com/uploads/images/medium_portrait/161/402691.jpg2
 
4.0%
https://static.tvmaze.com/uploads/images/medium_portrait/291/727817.jpg2
 
4.0%
https://static.tvmaze.com/uploads/images/medium_portrait/291/729147.jpg2
 
4.0%
https://static.tvmaze.com/uploads/images/medium_portrait/291/729461.jpg2
 
4.0%
https://static.tvmaze.com/uploads/images/medium_portrait/269/674781.jpg2
 
4.0%
https://static.tvmaze.com/uploads/images/medium_portrait/51/128137.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/medium_portrait/389/973148.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/medium_portrait/297/743345.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/medium_portrait/370/926754.jpg1
 
2.0%
Other values (27)27
54.0%

Length

2022-09-04T23:46:32.123949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_portrait/290/727350.jpg8
 
16.3%
https://static.tvmaze.com/uploads/images/medium_portrait/291/727817.jpg2
 
4.1%
https://static.tvmaze.com/uploads/images/medium_portrait/291/729147.jpg2
 
4.1%
https://static.tvmaze.com/uploads/images/medium_portrait/291/729461.jpg2
 
4.1%
https://static.tvmaze.com/uploads/images/medium_portrait/269/674781.jpg2
 
4.1%
https://static.tvmaze.com/uploads/images/medium_portrait/161/402691.jpg2
 
4.1%
https://static.tvmaze.com/uploads/images/medium_portrait/285/713990.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/medium_portrait/299/748854.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/medium_portrait/273/683332.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/medium_portrait/292/731348.jpg1
 
2.0%
Other values (27)27
55.1%

Most occurring characters

ValueCountFrequency (%)
/343
 
9.9%
t343
 
9.9%
a245
 
7.0%
m245
 
7.0%
p196
 
5.6%
s196
 
5.6%
i196
 
5.6%
.147
 
4.2%
e147
 
4.2%
o147
 
4.2%
Other values (22)1272
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2450
70.5%
Other Punctuation539
 
15.5%
Decimal Number439
 
12.6%
Connector Punctuation49
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t343
14.0%
a245
10.0%
m245
10.0%
p196
 
8.0%
s196
 
8.0%
i196
 
8.0%
e147
 
6.0%
o147
 
6.0%
d98
 
4.0%
u98
 
4.0%
Other values (8)539
22.0%
Decimal Number
ValueCountFrequency (%)
266
15.0%
764
14.6%
956
12.8%
156
12.8%
040
9.1%
537
8.4%
331
7.1%
431
7.1%
630
6.8%
828
6.4%
Other Punctuation
ValueCountFrequency (%)
/343
63.6%
.147
27.3%
:49
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2450
70.5%
Common1027
29.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t343
14.0%
a245
10.0%
m245
10.0%
p196
 
8.0%
s196
 
8.0%
i196
 
8.0%
e147
 
6.0%
o147
 
6.0%
d98
 
4.0%
u98
 
4.0%
Other values (8)539
22.0%
Common
ValueCountFrequency (%)
/343
33.4%
.147
14.3%
266
 
6.4%
764
 
6.2%
956
 
5.5%
156
 
5.5%
_49
 
4.8%
:49
 
4.8%
040
 
3.9%
537
 
3.6%
Other values (4)120
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3477
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/343
 
9.9%
t343
 
9.9%
a245
 
7.0%
m245
 
7.0%
p196
 
5.6%
s196
 
5.6%
i196
 
5.6%
.147
 
4.2%
e147
 
4.2%
o147
 
4.2%
Other values (22)1272
36.6%

_embedded.show.image.original
Categorical

HIGH CORRELATION
MISSING

Distinct37
Distinct (%)75.5%
Missing1
Missing (%)2.0%
Memory size528.0 B
https://static.tvmaze.com/uploads/images/original_untouched/290/727350.jpg
https://static.tvmaze.com/uploads/images/original_untouched/161/402691.jpg
 
2
https://static.tvmaze.com/uploads/images/original_untouched/291/727817.jpg
 
2
https://static.tvmaze.com/uploads/images/original_untouched/291/729147.jpg
 
2
https://static.tvmaze.com/uploads/images/original_untouched/291/729461.jpg
 
2
Other values (32)
33 

Length

Max length75
Median length74
Mean length73.95918367
Min length73

Characters and Unicode

Total characters3624
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)63.3%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/original_untouched/51/128137.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/original_untouched/69/173595.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/original_untouched/279/698895.jpg
4th rowhttps://static.tvmaze.com/uploads/images/original_untouched/286/715165.jpg
5th rowhttps://static.tvmaze.com/uploads/images/original_untouched/299/748854.jpg

Common Values

ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/290/727350.jpg8
 
16.0%
https://static.tvmaze.com/uploads/images/original_untouched/161/402691.jpg2
 
4.0%
https://static.tvmaze.com/uploads/images/original_untouched/291/727817.jpg2
 
4.0%
https://static.tvmaze.com/uploads/images/original_untouched/291/729147.jpg2
 
4.0%
https://static.tvmaze.com/uploads/images/original_untouched/291/729461.jpg2
 
4.0%
https://static.tvmaze.com/uploads/images/original_untouched/269/674781.jpg2
 
4.0%
https://static.tvmaze.com/uploads/images/original_untouched/51/128137.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/original_untouched/389/973148.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/original_untouched/297/743345.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/original_untouched/370/926754.jpg1
 
2.0%
Other values (27)27
54.0%

Length

2022-09-04T23:46:32.210949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/290/727350.jpg8
 
16.3%
https://static.tvmaze.com/uploads/images/original_untouched/291/727817.jpg2
 
4.1%
https://static.tvmaze.com/uploads/images/original_untouched/291/729147.jpg2
 
4.1%
https://static.tvmaze.com/uploads/images/original_untouched/291/729461.jpg2
 
4.1%
https://static.tvmaze.com/uploads/images/original_untouched/269/674781.jpg2
 
4.1%
https://static.tvmaze.com/uploads/images/original_untouched/161/402691.jpg2
 
4.1%
https://static.tvmaze.com/uploads/images/original_untouched/285/713990.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/original_untouched/299/748854.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/original_untouched/273/683332.jpg1
 
2.0%
https://static.tvmaze.com/uploads/images/original_untouched/292/731348.jpg1
 
2.0%
Other values (27)27
55.1%

Most occurring characters

ValueCountFrequency (%)
/343
 
9.5%
t294
 
8.1%
a245
 
6.8%
s196
 
5.4%
i196
 
5.4%
o196
 
5.4%
p147
 
4.1%
c147
 
4.1%
.147
 
4.1%
g147
 
4.1%
Other values (23)1566
43.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2597
71.7%
Other Punctuation539
 
14.9%
Decimal Number439
 
12.1%
Connector Punctuation49
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t294
 
11.3%
a245
 
9.4%
s196
 
7.5%
i196
 
7.5%
o196
 
7.5%
p147
 
5.7%
c147
 
5.7%
g147
 
5.7%
m147
 
5.7%
e147
 
5.7%
Other values (9)735
28.3%
Decimal Number
ValueCountFrequency (%)
266
15.0%
764
14.6%
956
12.8%
156
12.8%
040
9.1%
537
8.4%
331
7.1%
431
7.1%
630
6.8%
828
6.4%
Other Punctuation
ValueCountFrequency (%)
/343
63.6%
.147
27.3%
:49
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2597
71.7%
Common1027
 
28.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t294
 
11.3%
a245
 
9.4%
s196
 
7.5%
i196
 
7.5%
o196
 
7.5%
p147
 
5.7%
c147
 
5.7%
g147
 
5.7%
m147
 
5.7%
e147
 
5.7%
Other values (9)735
28.3%
Common
ValueCountFrequency (%)
/343
33.4%
.147
14.3%
266
 
6.4%
764
 
6.2%
956
 
5.5%
156
 
5.5%
:49
 
4.8%
_49
 
4.8%
040
 
3.9%
537
 
3.6%
Other values (4)120
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3624
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/343
 
9.5%
t294
 
8.1%
a245
 
6.8%
s196
 
5.4%
i196
 
5.4%
o196
 
5.4%
p147
 
4.1%
c147
 
4.1%
.147
 
4.1%
g147
 
4.1%
Other values (23)1566
43.2%

_embedded.show.summary
Categorical

HIGH CORRELATION
MISSING

Distinct32
Distinct (%)72.7%
Missing6
Missing (%)12.0%
Memory size528.0 B
<p>In the middle of the night, far north in Norway at Finnmarksvidda, the Sami teenager Elle Jannok is on her way home from a party, when she finds a cell phone ringing. The phone belongs to a missing girl, Sofie. A couple of days later, the local police discover Sofie's body in a caravan three hours south of Finnmark.</p><p>Maja Angell, who is studying at the University of London, is defending her highly debated doctoral thesis on criminology &amp; profiling as she hears about the murder case in her old home town in Northern Norway. She defies all warnings and leaves the University to travel North. She has a message for the local police: the man charged with the murder, is not the killer. But no one's willing to listen to her theories. Not until Maja dares to delve into her own most dangerous, repressed memories of childhood will she be able to stop him and soon she realize that the killer is closer than she ever imagined.</p>
<p><b>Fancy Nancy</b> centers around six-year-old Nancy, a girl who likes to be fancy in everything from her advanced vocabulary to her creative, elaborate attire. Excited to experience what the magnificent world has to offer, Nancy uses her ingenuity and imagination as she learns that while life doesn't always go as planned, it's important to celebrate the differences that make everyone unique.</p>
 
2
<p>The story of the "unique romance" about teenagers navigating love, friendship, and the chaos of high school. The story will focus mainly on Gong Joo Young, a cute and lovable student who falls in love at first sight with the standoffish and popular Wang Ja Rim. Unwavering in his love for Wang Ja Rim, the confident and cheerful Gong Joo Young isn't shy about his feelings and is determined to win her heart no matter what. However, despite his gentle, sweet nature when it comes to love, Gong Joo Young is also a steadfast, loyal friend who shows off a tougher and more mature side when his friends need his help.</p>
 
2
<p>Ju Xuanwen (Wan Yan Lo-yun) is a man with a noble appearance and many virtues. It is a pity that he fell ill with neurosis at a young age - after an unexplained car accident he falls into a delusional state and considers himself a prince. Since then, he no longer cares about the activities of his company and concentrates on becoming emperor.<br />Lo Huai (Chuang Da Fei) - psychiatrist on the verge of bankruptcy. Because of the need for money, she took responsibility for the treatment of Ju Xuanwen. However, she did not expect her peaceful days to end one day. Spending time together, they began to fall in love with each other.</p>
 
2
<p>A story that follows undercover cop Gan Tian Lei who spent 10 years of his life walking a gray area. After waking up from a serious injury, he restarts his life and tries to solve a case by relying on his lost memories.</p>
 
2
Other values (27)
28 

Length

Max length941
Median length554
Mean length493.7045455
Min length96

Characters and Unicode

Total characters21723
Distinct characters79
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)59.1%

Sample

1st row<p>Zero was mankind's first real superhero. Under his watch, countless other superheros appeared and followed in his footsteps. However, after 5 years of war, Zero disappeared without a trace.<br /><br />(Source: zeroscans)</p>
2nd row<p>One day, an unexplained RR virus appeared on the earth, drawing the world into disaster. Infected animals mutated into terrible monsters, invaded massively, and humans built walls around the destruction and established the base city as the last bastion for humans. The suffering that mankind has experienced during this period of time is known as the "Great Nirvana Period." Not only that, Luo Feng not only carried the burden of supporting the family but also to protect the human homeland, for the better survival and development of mankind, together with other justice warriors, to join hands against the fierce monsters. Under the desperate situation of the end, can Luo Feng and other warriors repel monsters and successfully protect the human world?</p>
3rd row<p>The protagonist Qin Chen, who was originally the top genius in the military domain, was conspired by the people to fall into the death canyon in the forbidden land of the mainland. Qin Chen, who was inevitably dead, unexpectedly triggered the power of the mysterious ancient sword.<br /><br />Three hundred years later, in a remote part of the Tianwu mainland, a boy of the same name accidentally inherited Qin Chen's will. As the beloved grandson of King Dingwu of the Daqi National Army, due to the birth father's birth, the mother and son were treated coldly in Dingwu's palace and lived together. In order to rewrite the myth of the strong man in hope of the sun, and to protect everything he loves, Qin Chen resolutely took up the responsibility of maintaining the five kingdoms of the world and set foot on the road of martial arts again.</p>
4th row<p>In the twenty-first century, gods and demons can no longer maintain balance due to the rapid development of human society. In an effort to restore proper order, the gods began to take care of saving the world, for which they sent a group of gods and monsters to the world of people, who must find there the " key " to salvation. Su moting is a girl with the personality of "demon child". When her parents asked her to leave home so that she could become independent and independent, she met the beautiful and charming God of Tianjin and the mysterious demon cat. So begins a new turbulent round of su moting's life.</p><p><br /> </p>
5th row<p>The disciples of the Lingchuan Sect have guarded the Fans of Heaven and Earth for nearly a century. Mu Yun and Hua Yue are the only disciples of the sect that are left. The stubborn and disobedient Hua Yue unintentionally discovers that the Fan of Heaven possesses the power to travel through time. To escape being forced to study and practice martial arts by Mu Yun, Hua Yue travels to the future to have fun. Hundreds of years in the future she meets Xiao Qian who looks exactly like her. Secrets come to the surface, and adventures take place.</p>

Common Values

ValueCountFrequency (%)
<p>In the middle of the night, far north in Norway at Finnmarksvidda, the Sami teenager Elle Jannok is on her way home from a party, when she finds a cell phone ringing. The phone belongs to a missing girl, Sofie. A couple of days later, the local police discover Sofie's body in a caravan three hours south of Finnmark.</p><p>Maja Angell, who is studying at the University of London, is defending her highly debated doctoral thesis on criminology &amp; profiling as she hears about the murder case in her old home town in Northern Norway. She defies all warnings and leaves the University to travel North. She has a message for the local police: the man charged with the murder, is not the killer. But no one's willing to listen to her theories. Not until Maja dares to delve into her own most dangerous, repressed memories of childhood will she be able to stop him and soon she realize that the killer is closer than she ever imagined.</p>8
 
16.0%
<p><b>Fancy Nancy</b> centers around six-year-old Nancy, a girl who likes to be fancy in everything from her advanced vocabulary to her creative, elaborate attire. Excited to experience what the magnificent world has to offer, Nancy uses her ingenuity and imagination as she learns that while life doesn't always go as planned, it's important to celebrate the differences that make everyone unique.</p>2
 
4.0%
<p>The story of the "unique romance" about teenagers navigating love, friendship, and the chaos of high school. The story will focus mainly on Gong Joo Young, a cute and lovable student who falls in love at first sight with the standoffish and popular Wang Ja Rim. Unwavering in his love for Wang Ja Rim, the confident and cheerful Gong Joo Young isn't shy about his feelings and is determined to win her heart no matter what. However, despite his gentle, sweet nature when it comes to love, Gong Joo Young is also a steadfast, loyal friend who shows off a tougher and more mature side when his friends need his help.</p>2
 
4.0%
<p>Ju Xuanwen (Wan Yan Lo-yun) is a man with a noble appearance and many virtues. It is a pity that he fell ill with neurosis at a young age - after an unexplained car accident he falls into a delusional state and considers himself a prince. Since then, he no longer cares about the activities of his company and concentrates on becoming emperor.<br />Lo Huai (Chuang Da Fei) - psychiatrist on the verge of bankruptcy. Because of the need for money, she took responsibility for the treatment of Ju Xuanwen. However, she did not expect her peaceful days to end one day. Spending time together, they began to fall in love with each other.</p>2
 
4.0%
<p>A story that follows undercover cop Gan Tian Lei who spent 10 years of his life walking a gray area. After waking up from a serious injury, he restarts his life and tries to solve a case by relying on his lost memories.</p>2
 
4.0%
<p>Ke Ying is a talented economics lecturer who is forced to help psychopath Feng Xiao Sheng gain real power within his corporation. The street smart Xiao Wu is a police informant, and when he discovers Fu's company is laundering money with foreign bank accounts, he uses his position as Feng Xiao Sheng's right-hand man to collect evidence. He befriends Ke Ying, and the two work together to destroy the criminal organization.</p>2
 
4.0%
<p><b>Peyton's Places</b> offers a fun, insightful tour through 100 years of football, following the sport and the league's rise to an American cultural touchstone. For nearly a year, Manning has crisscrossed the country, visiting the people and places that have played an important part in the making of the NFL—highlighting memorable events, teams, players, and trends over the past century.</p>1
 
2.0%
<p>A group of aspiring idols gather at Takanashi Productions and are entrusted with the company's future. The seven men who have just met represent a variety of totally different personalities. However, they each have their own charm and possess unknown potential as idols. Forming a group, they take their first step together as <b>IDOLiSH7</b>. Their brilliantly shining dancing forms onstage eventually begin captivating the hearts of the people. In the glorious but sometimes harsh world of idols, they aim for the top with dreams in their hearts!</p>1
 
2.0%
<p>The lads and lasses of Achievement Hunter congregate each week to discuss the important questions in life. Plus drink beer.</p>1
 
2.0%
<p>Your first podcast of the week is the last word in tech. Join the top tech pundits in a roundtable discussion of the latest trends in high tech. Hosted by Leo Laporte and friends.</p>1
 
2.0%
Other values (22)22
44.0%
(Missing)6
 
12.0%

Length

2022-09-04T23:46:32.304949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the254
 
6.9%
to120
 
3.2%
of116
 
3.1%
a114
 
3.1%
and97
 
2.6%
is73
 
2.0%
in70
 
1.9%
she67
 
1.8%
her57
 
1.5%
his34
 
0.9%
Other values (1003)2698
72.9%

Most occurring characters

ValueCountFrequency (%)
3644
16.8%
e2080
 
9.6%
t1370
 
6.3%
o1337
 
6.2%
a1321
 
6.1%
n1279
 
5.9%
i1224
 
5.6%
r1072
 
4.9%
s1059
 
4.9%
h976
 
4.5%
Other values (69)6361
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter16594
76.4%
Space Separator3657
 
16.8%
Other Punctuation553
 
2.5%
Uppercase Letter552
 
2.5%
Math Symbol304
 
1.4%
Dash Punctuation34
 
0.2%
Decimal Number17
 
0.1%
Close Punctuation6
 
< 0.1%
Open Punctuation6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e2080
12.5%
t1370
 
8.3%
o1337
 
8.1%
a1321
 
8.0%
n1279
 
7.7%
i1224
 
7.4%
r1072
 
6.5%
s1059
 
6.4%
h976
 
5.9%
l734
 
4.4%
Other values (16)4142
25.0%
Uppercase Letter
ValueCountFrequency (%)
S69
 
12.5%
N55
 
10.0%
T48
 
8.7%
F42
 
7.6%
A34
 
6.2%
L28
 
5.1%
M26
 
4.7%
H26
 
4.7%
U25
 
4.5%
J24
 
4.3%
Other values (16)175
31.7%
Other Punctuation
ValueCountFrequency (%)
,203
36.7%
.170
30.7%
/80
 
14.5%
'44
 
8.0%
"15
 
2.7%
!12
 
2.2%
:11
 
2.0%
&8
 
1.4%
;8
 
1.4%
?2
 
0.4%
Decimal Number
ValueCountFrequency (%)
06
35.3%
14
23.5%
52
 
11.8%
41
 
5.9%
71
 
5.9%
81
 
5.9%
21
 
5.9%
31
 
5.9%
Dash Punctuation
ValueCountFrequency (%)
-25
73.5%
8
 
23.5%
1
 
2.9%
Space Separator
ValueCountFrequency (%)
3644
99.6%
 13
 
0.4%
Math Symbol
ValueCountFrequency (%)
<152
50.0%
>152
50.0%
Close Punctuation
ValueCountFrequency (%)
)6
100.0%
Open Punctuation
ValueCountFrequency (%)
(6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17146
78.9%
Common4577
 
21.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e2080
12.1%
t1370
 
8.0%
o1337
 
7.8%
a1321
 
7.7%
n1279
 
7.5%
i1224
 
7.1%
r1072
 
6.3%
s1059
 
6.2%
h976
 
5.7%
l734
 
4.3%
Other values (42)4694
27.4%
Common
ValueCountFrequency (%)
3644
79.6%
,203
 
4.4%
.170
 
3.7%
<152
 
3.3%
>152
 
3.3%
/80
 
1.7%
'44
 
1.0%
-25
 
0.5%
"15
 
0.3%
 13
 
0.3%
Other values (17)79
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII21701
99.9%
None13
 
0.1%
Punctuation9
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3644
16.8%
e2080
 
9.6%
t1370
 
6.3%
o1337
 
6.2%
a1321
 
6.1%
n1279
 
5.9%
i1224
 
5.6%
r1072
 
4.9%
s1059
 
4.9%
h976
 
4.5%
Other values (66)6339
29.2%
None
ValueCountFrequency (%)
 13
100.0%
Punctuation
ValueCountFrequency (%)
8
88.9%
1
 
11.1%

_embedded.show.updated
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct38
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1636602718
Minimum1603467037
Maximum1662305316
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-09-04T23:46:32.395949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1603467037
5-th percentile1610257007
Q11614642259
median1646413120
Q31654345330
95-th percentile1661741206
Maximum1662305316
Range58838279
Interquartile range (IQR)39703070.5

Descriptive statistics

Standard deviation19503245.23
Coefficient of variation (CV)0.01191690874
Kurtosis-1.666401731
Mean1636602718
Median Absolute Deviation (MAD)15143118.5
Skewness-0.2405534796
Sum8.183013589 × 1010
Variance3.803765744 × 1014
MonotonicityNot monotonic
2022-09-04T23:46:32.484139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
16146422598
 
16.0%
16573745032
 
4.0%
16549762522
 
4.0%
16549764112
 
4.0%
16154510692
 
4.0%
16096167882
 
4.0%
16620631391
 
2.0%
16470729551
 
2.0%
16457532861
 
2.0%
16443270091
 
2.0%
Other values (28)28
56.0%
ValueCountFrequency (%)
16034670371
 
2.0%
16096167882
 
4.0%
16110394971
 
2.0%
16115079791
 
2.0%
16120609221
 
2.0%
16129809601
 
2.0%
16146422598
16.0%
16154510692
 
4.0%
16215672851
 
2.0%
16236114051
 
2.0%
ValueCountFrequency (%)
16623053161
2.0%
16620631391
2.0%
16619598561
2.0%
16614739671
2.0%
16612547171
2.0%
16573745032
4.0%
16551457591
2.0%
16549764112
4.0%
16549762522
4.0%
16547096541
2.0%

_embedded.show._links.self.href
Categorical

HIGH CORRELATION

Distinct38
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
https://api.tvmaze.com/shows/52653
https://api.tvmaze.com/shows/34940
 
2
https://api.tvmaze.com/shows/52685
 
2
https://api.tvmaze.com/shows/52743
 
2
https://api.tvmaze.com/shows/52781
 
2
Other values (33)
34 

Length

Max length34
Median length34
Mean length34
Min length34

Characters and Unicode

Total characters1700
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)64.0%

Sample

1st rowhttps://api.tvmaze.com/shows/10892
2nd rowhttps://api.tvmaze.com/shows/19628
3rd rowhttps://api.tvmaze.com/shows/51471
4th rowhttps://api.tvmaze.com/shows/52178
5th rowhttps://api.tvmaze.com/shows/54033

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/shows/526538
 
16.0%
https://api.tvmaze.com/shows/349402
 
4.0%
https://api.tvmaze.com/shows/526852
 
4.0%
https://api.tvmaze.com/shows/527432
 
4.0%
https://api.tvmaze.com/shows/527812
 
4.0%
https://api.tvmaze.com/shows/499482
 
4.0%
https://api.tvmaze.com/shows/108921
 
2.0%
https://api.tvmaze.com/shows/599511
 
2.0%
https://api.tvmaze.com/shows/536691
 
2.0%
https://api.tvmaze.com/shows/586451
 
2.0%
Other values (28)28
56.0%

Length

2022-09-04T23:46:32.560137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/shows/526538
 
16.0%
https://api.tvmaze.com/shows/526852
 
4.0%
https://api.tvmaze.com/shows/527432
 
4.0%
https://api.tvmaze.com/shows/527812
 
4.0%
https://api.tvmaze.com/shows/499482
 
4.0%
https://api.tvmaze.com/shows/349402
 
4.0%
https://api.tvmaze.com/shows/495241
 
2.0%
https://api.tvmaze.com/shows/540331
 
2.0%
https://api.tvmaze.com/shows/503981
 
2.0%
https://api.tvmaze.com/shows/528981
 
2.0%
Other values (28)28
56.0%

Most occurring characters

ValueCountFrequency (%)
/200
 
11.8%
s150
 
8.8%
t150
 
8.8%
h100
 
5.9%
p100
 
5.9%
a100
 
5.9%
.100
 
5.9%
o100
 
5.9%
m100
 
5.9%
e50
 
2.9%
Other values (16)550
32.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1100
64.7%
Other Punctuation350
 
20.6%
Decimal Number250
 
14.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s150
13.6%
t150
13.6%
h100
9.1%
p100
9.1%
a100
9.1%
o100
9.1%
m100
9.1%
e50
 
4.5%
w50
 
4.5%
c50
 
4.5%
Other values (3)150
13.6%
Decimal Number
ValueCountFrequency (%)
550
20.0%
332
12.8%
228
11.2%
428
11.2%
925
10.0%
624
9.6%
819
 
7.6%
119
 
7.6%
013
 
5.2%
712
 
4.8%
Other Punctuation
ValueCountFrequency (%)
/200
57.1%
.100
28.6%
:50
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin1100
64.7%
Common600
35.3%

Most frequent character per script

Common
ValueCountFrequency (%)
/200
33.3%
.100
16.7%
550
 
8.3%
:50
 
8.3%
332
 
5.3%
228
 
4.7%
428
 
4.7%
925
 
4.2%
624
 
4.0%
819
 
3.2%
Other values (3)44
 
7.3%
Latin
ValueCountFrequency (%)
s150
13.6%
t150
13.6%
h100
9.1%
p100
9.1%
a100
9.1%
o100
9.1%
m100
9.1%
e50
 
4.5%
w50
 
4.5%
c50
 
4.5%
Other values (3)150
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/200
 
11.8%
s150
 
8.8%
t150
 
8.8%
h100
 
5.9%
p100
 
5.9%
a100
 
5.9%
.100
 
5.9%
o100
 
5.9%
m100
 
5.9%
e50
 
2.9%
Other values (16)550
32.4%
Distinct38
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
https://api.tvmaze.com/episodes/1993654
https://api.tvmaze.com/episodes/2358798
 
2
https://api.tvmaze.com/episodes/2118097
 
2
https://api.tvmaze.com/episodes/1997552
 
2
https://api.tvmaze.com/episodes/1998564
 
2
Other values (33)
34 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters1950
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)64.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/2382770
2nd rowhttps://api.tvmaze.com/episodes/2380279
3rd rowhttps://api.tvmaze.com/episodes/1956341
4th rowhttps://api.tvmaze.com/episodes/2259040
5th rowhttps://api.tvmaze.com/episodes/2309441

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19936548
 
16.0%
https://api.tvmaze.com/episodes/23587982
 
4.0%
https://api.tvmaze.com/episodes/21180972
 
4.0%
https://api.tvmaze.com/episodes/19975522
 
4.0%
https://api.tvmaze.com/episodes/19985642
 
4.0%
https://api.tvmaze.com/episodes/19740562
 
4.0%
https://api.tvmaze.com/episodes/23827701
 
2.0%
https://api.tvmaze.com/episodes/22559551
 
2.0%
https://api.tvmaze.com/episodes/20343991
 
2.0%
https://api.tvmaze.com/episodes/22044541
 
2.0%
Other values (28)28
56.0%

Length

2022-09-04T23:46:32.625137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19936548
 
16.0%
https://api.tvmaze.com/episodes/21180972
 
4.0%
https://api.tvmaze.com/episodes/19975522
 
4.0%
https://api.tvmaze.com/episodes/19985642
 
4.0%
https://api.tvmaze.com/episodes/19740562
 
4.0%
https://api.tvmaze.com/episodes/23587982
 
4.0%
https://api.tvmaze.com/episodes/19780171
 
2.0%
https://api.tvmaze.com/episodes/23094411
 
2.0%
https://api.tvmaze.com/episodes/20123271
 
2.0%
https://api.tvmaze.com/episodes/20057621
 
2.0%
Other values (28)28
56.0%

Most occurring characters

ValueCountFrequency (%)
/200
 
10.3%
t150
 
7.7%
p150
 
7.7%
s150
 
7.7%
e150
 
7.7%
a100
 
5.1%
i100
 
5.1%
.100
 
5.1%
m100
 
5.1%
o100
 
5.1%
Other values (16)650
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1250
64.1%
Other Punctuation350
 
17.9%
Decimal Number350
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t150
12.0%
p150
12.0%
s150
12.0%
e150
12.0%
a100
8.0%
i100
8.0%
m100
8.0%
o100
8.0%
h50
 
4.0%
d50
 
4.0%
Other values (3)150
12.0%
Decimal Number
ValueCountFrequency (%)
950
14.3%
247
13.4%
145
12.9%
536
10.3%
333
9.4%
432
9.1%
731
8.9%
027
7.7%
825
7.1%
624
6.9%
Other Punctuation
ValueCountFrequency (%)
/200
57.1%
.100
28.6%
:50
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin1250
64.1%
Common700
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/200
28.6%
.100
14.3%
950
 
7.1%
:50
 
7.1%
247
 
6.7%
145
 
6.4%
536
 
5.1%
333
 
4.7%
432
 
4.6%
731
 
4.4%
Other values (3)76
 
10.9%
Latin
ValueCountFrequency (%)
t150
12.0%
p150
12.0%
s150
12.0%
e150
12.0%
a100
8.0%
i100
8.0%
m100
8.0%
o100
8.0%
h50
 
4.0%
d50
 
4.0%
Other values (3)150
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/200
 
10.3%
t150
 
7.7%
p150
 
7.7%
s150
 
7.7%
e150
 
7.7%
a100
 
5.1%
i100
 
5.1%
.100
 
5.1%
m100
 
5.1%
o100
 
5.1%
Other values (16)650
33.3%

image
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50
Missing (%)100.0%
Memory size528.0 B
Distinct2
Distinct (%)100.0%
Missing48
Missing (%)96.0%
Memory size528.0 B
https://api.tvmaze.com/episodes/2259041
https://api.tvmaze.com/episodes/2309442

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters78
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/2259041
2nd rowhttps://api.tvmaze.com/episodes/2309442

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/22590411
 
2.0%
https://api.tvmaze.com/episodes/23094421
 
2.0%
(Missing)48
96.0%

Length

2022-09-04T23:46:32.692135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:46:32.764292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/22590411
50.0%
https://api.tvmaze.com/episodes/23094421
50.0%

Most occurring characters

ValueCountFrequency (%)
/8
 
10.3%
p6
 
7.7%
s6
 
7.7%
t6
 
7.7%
e6
 
7.7%
24
 
5.1%
a4
 
5.1%
i4
 
5.1%
.4
 
5.1%
m4
 
5.1%
Other values (13)26
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter50
64.1%
Other Punctuation14
 
17.9%
Decimal Number14
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p6
12.0%
s6
12.0%
t6
12.0%
e6
12.0%
a4
8.0%
i4
8.0%
m4
8.0%
o4
8.0%
h2
 
4.0%
z2
 
4.0%
Other values (3)6
12.0%
Decimal Number
ValueCountFrequency (%)
24
28.6%
43
21.4%
02
14.3%
92
14.3%
51
 
7.1%
11
 
7.1%
31
 
7.1%
Other Punctuation
ValueCountFrequency (%)
/8
57.1%
.4
28.6%
:2
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin50
64.1%
Common28
35.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
p6
12.0%
s6
12.0%
t6
12.0%
e6
12.0%
a4
8.0%
i4
8.0%
m4
8.0%
o4
8.0%
h2
 
4.0%
z2
 
4.0%
Other values (3)6
12.0%
Common
ValueCountFrequency (%)
/8
28.6%
24
14.3%
.4
14.3%
43
 
10.7%
02
 
7.1%
92
 
7.1%
:2
 
7.1%
51
 
3.6%
11
 
3.6%
31
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII78
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/8
 
10.3%
p6
 
7.7%
s6
 
7.7%
t6
 
7.7%
e6
 
7.7%
24
 
5.1%
a4
 
5.1%
i4
 
5.1%
.4
 
5.1%
m4
 
5.1%
Other values (13)26
33.3%

_embedded.show.network.id
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct3
Distinct (%)100.0%
Missing47
Missing (%)94.0%
Memory size528.0 B
8.0
374.0
132.0

Length

Max length5
Median length5
Mean length4.333333333
Min length3

Characters and Unicode

Total characters13
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row8.0
2nd row374.0
3rd row132.0

Common Values

ValueCountFrequency (%)
8.01
 
2.0%
374.01
 
2.0%
132.01
 
2.0%
(Missing)47
94.0%

Length

2022-09-04T23:46:32.839295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:46:32.916302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
8.01
33.3%
374.01
33.3%
132.01
33.3%

Most occurring characters

ValueCountFrequency (%)
.3
23.1%
03
23.1%
32
15.4%
81
 
7.7%
71
 
7.7%
41
 
7.7%
11
 
7.7%
21
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10
76.9%
Other Punctuation3
 
23.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03
30.0%
32
20.0%
81
 
10.0%
71
 
10.0%
41
 
10.0%
11
 
10.0%
21
 
10.0%
Other Punctuation
ValueCountFrequency (%)
.3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common13
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.3
23.1%
03
23.1%
32
15.4%
81
 
7.7%
71
 
7.7%
41
 
7.7%
11
 
7.7%
21
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII13
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.3
23.1%
03
23.1%
32
15.4%
81
 
7.7%
71
 
7.7%
41
 
7.7%
11
 
7.7%
21
 
7.7%

_embedded.show.network.name
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct3
Distinct (%)100.0%
Missing47
Missing (%)94.0%
Memory size528.0 B
HBO
TV Globo
Tokyo MX

Length

Max length8
Median length8
Mean length6.333333333
Min length3

Characters and Unicode

Total characters19
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowHBO
2nd rowTV Globo
3rd rowTokyo MX

Common Values

ValueCountFrequency (%)
HBO1
 
2.0%
TV Globo1
 
2.0%
Tokyo MX1
 
2.0%
(Missing)47
94.0%

Length

2022-09-04T23:46:32.982291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:46:33.054254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
hbo1
20.0%
tv1
20.0%
globo1
20.0%
tokyo1
20.0%
mx1
20.0%

Most occurring characters

ValueCountFrequency (%)
o4
21.1%
T2
10.5%
2
10.5%
H1
 
5.3%
B1
 
5.3%
O1
 
5.3%
V1
 
5.3%
G1
 
5.3%
l1
 
5.3%
b1
 
5.3%
Other values (4)4
21.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter9
47.4%
Lowercase Letter8
42.1%
Space Separator2
 
10.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T2
22.2%
H1
11.1%
B1
11.1%
O1
11.1%
V1
11.1%
G1
11.1%
M1
11.1%
X1
11.1%
Lowercase Letter
ValueCountFrequency (%)
o4
50.0%
l1
 
12.5%
b1
 
12.5%
k1
 
12.5%
y1
 
12.5%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17
89.5%
Common2
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o4
23.5%
T2
11.8%
H1
 
5.9%
B1
 
5.9%
O1
 
5.9%
V1
 
5.9%
G1
 
5.9%
l1
 
5.9%
b1
 
5.9%
k1
 
5.9%
Other values (3)3
17.6%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII19
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o4
21.1%
T2
10.5%
2
10.5%
H1
 
5.3%
B1
 
5.3%
O1
 
5.3%
V1
 
5.3%
G1
 
5.3%
l1
 
5.3%
b1
 
5.3%
Other values (4)4
21.1%

_embedded.show.network.country.name
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct3
Distinct (%)100.0%
Missing47
Missing (%)94.0%
Memory size528.0 B
United States
Brazil
Japan

Length

Max length13
Median length6
Mean length8
Min length5

Characters and Unicode

Total characters24
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowUnited States
2nd rowBrazil
3rd rowJapan

Common Values

ValueCountFrequency (%)
United States1
 
2.0%
Brazil1
 
2.0%
Japan1
 
2.0%
(Missing)47
94.0%

Length

2022-09-04T23:46:33.124574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:46:33.200571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
united1
25.0%
states1
25.0%
brazil1
25.0%
japan1
25.0%

Most occurring characters

ValueCountFrequency (%)
a4
16.7%
t3
12.5%
n2
 
8.3%
i2
 
8.3%
e2
 
8.3%
U1
 
4.2%
d1
 
4.2%
1
 
4.2%
S1
 
4.2%
s1
 
4.2%
Other values (6)6
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter19
79.2%
Uppercase Letter4
 
16.7%
Space Separator1
 
4.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a4
21.1%
t3
15.8%
n2
10.5%
i2
10.5%
e2
10.5%
d1
 
5.3%
s1
 
5.3%
r1
 
5.3%
z1
 
5.3%
l1
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
U1
25.0%
S1
25.0%
B1
25.0%
J1
25.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin23
95.8%
Common1
 
4.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a4
17.4%
t3
13.0%
n2
 
8.7%
i2
 
8.7%
e2
 
8.7%
U1
 
4.3%
d1
 
4.3%
S1
 
4.3%
s1
 
4.3%
B1
 
4.3%
Other values (5)5
21.7%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII24
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a4
16.7%
t3
12.5%
n2
 
8.3%
i2
 
8.3%
e2
 
8.3%
U1
 
4.2%
d1
 
4.2%
1
 
4.2%
S1
 
4.2%
s1
 
4.2%
Other values (6)6
25.0%

_embedded.show.network.country.code
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct3
Distinct (%)100.0%
Missing47
Missing (%)94.0%
Memory size528.0 B
US
BR
JP

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowUS
2nd rowBR
3rd rowJP

Common Values

ValueCountFrequency (%)
US1
 
2.0%
BR1
 
2.0%
JP1
 
2.0%
(Missing)47
94.0%

Length

2022-09-04T23:46:33.263584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:46:33.331579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
us1
33.3%
br1
33.3%
jp1
33.3%

Most occurring characters

ValueCountFrequency (%)
U1
16.7%
S1
16.7%
B1
16.7%
R1
16.7%
J1
16.7%
P1
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter6
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U1
16.7%
S1
16.7%
B1
16.7%
R1
16.7%
J1
16.7%
P1
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin6
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U1
16.7%
S1
16.7%
B1
16.7%
R1
16.7%
J1
16.7%
P1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII6
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U1
16.7%
S1
16.7%
B1
16.7%
R1
16.7%
J1
16.7%
P1
16.7%

_embedded.show.network.country.timezone
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct3
Distinct (%)100.0%
Missing47
Missing (%)94.0%
Memory size528.0 B
America/New_York
America/Noronha
Asia/Tokyo

Length

Max length16
Median length15
Mean length13.66666667
Min length10

Characters and Unicode

Total characters41
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st rowAmerica/New_York
2nd rowAmerica/Noronha
3rd rowAsia/Tokyo

Common Values

ValueCountFrequency (%)
America/New_York1
 
2.0%
America/Noronha1
 
2.0%
Asia/Tokyo1
 
2.0%
(Missing)47
94.0%

Length

2022-09-04T23:46:33.534809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:46:33.608736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
america/new_york1
33.3%
america/noronha1
33.3%
asia/tokyo1
33.3%

Most occurring characters

ValueCountFrequency (%)
o5
12.2%
r4
 
9.8%
a4
 
9.8%
A3
 
7.3%
e3
 
7.3%
i3
 
7.3%
/3
 
7.3%
c2
 
4.9%
N2
 
4.9%
m2
 
4.9%
Other values (9)10
24.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter30
73.2%
Uppercase Letter7
 
17.1%
Other Punctuation3
 
7.3%
Connector Punctuation1
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o5
16.7%
r4
13.3%
a4
13.3%
e3
10.0%
i3
10.0%
c2
 
6.7%
m2
 
6.7%
k2
 
6.7%
h1
 
3.3%
s1
 
3.3%
Other values (3)3
10.0%
Uppercase Letter
ValueCountFrequency (%)
A3
42.9%
N2
28.6%
T1
 
14.3%
Y1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
/3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin37
90.2%
Common4
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
o5
13.5%
r4
10.8%
a4
10.8%
A3
8.1%
e3
8.1%
i3
8.1%
c2
 
5.4%
N2
 
5.4%
m2
 
5.4%
k2
 
5.4%
Other values (7)7
18.9%
Common
ValueCountFrequency (%)
/3
75.0%
_1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII41
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o5
12.2%
r4
 
9.8%
a4
 
9.8%
A3
 
7.3%
e3
 
7.3%
i3
 
7.3%
/3
 
7.3%
c2
 
4.9%
N2
 
4.9%
m2
 
4.9%
Other values (9)10
24.4%

_embedded.show.network.officialSite
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing49
Missing (%)98.0%
Memory size528.0 B
https://www.hbo.com/

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters20
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowhttps://www.hbo.com/

Common Values

ValueCountFrequency (%)
https://www.hbo.com/1
 
2.0%
(Missing)49
98.0%

Length

2022-09-04T23:46:33.673806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-04T23:46:33.737257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
https://www.hbo.com1
100.0%

Most occurring characters

ValueCountFrequency (%)
/3
15.0%
w3
15.0%
h2
10.0%
t2
10.0%
.2
10.0%
o2
10.0%
p1
 
5.0%
s1
 
5.0%
:1
 
5.0%
b1
 
5.0%
Other values (2)2
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14
70.0%
Other Punctuation6
30.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w3
21.4%
h2
14.3%
t2
14.3%
o2
14.3%
p1
 
7.1%
s1
 
7.1%
b1
 
7.1%
c1
 
7.1%
m1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
/3
50.0%
.2
33.3%
:1
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin14
70.0%
Common6
30.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
w3
21.4%
h2
14.3%
t2
14.3%
o2
14.3%
p1
 
7.1%
s1
 
7.1%
b1
 
7.1%
c1
 
7.1%
m1
 
7.1%
Common
ValueCountFrequency (%)
/3
50.0%
.2
33.3%
:1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII20
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/3
15.0%
w3
15.0%
h2
10.0%
t2
10.0%
.2
10.0%
o2
10.0%
p1
 
5.0%
s1
 
5.0%
:1
 
5.0%
b1
 
5.0%
Other values (2)2
10.0%

_embedded.show.webChannel
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50
Missing (%)100.0%
Memory size528.0 B

_embedded.show.webChannel.country
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50
Missing (%)100.0%
Memory size528.0 B

_embedded.show.image
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing50
Missing (%)100.0%
Memory size528.0 B

Interactions

2022-09-04T23:46:23.792150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:13.315153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:14.397755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:15.355028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:16.411419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:17.306240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:18.314427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:19.122477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:20.112181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:20.998551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:21.885310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:22.871541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:23.869703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:13.531190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:14.482421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:15.437028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:16.490419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:17.387240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:18.384418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:19.192463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:20.185112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:21.075552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:21.964310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:22.951573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:23.941705image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:13.605115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:14.572827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:15.518826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:16.570419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:17.458240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:18.454410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:19.265856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:20.262191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:21.152552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:22.042418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:23.032573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:24.011635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:13.682820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:14.653965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:15.596826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:16.641420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:17.539310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:18.524409image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:19.329856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:20.337180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:21.227551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:22.254417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:23.109573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:24.083201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:13.760209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:14.733965image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:15.809885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:16.715419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:17.615312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:18.590414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:19.399860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:20.408181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:21.299551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:22.327417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:23.184572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:24.163201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:13.840208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:14.806562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:15.888429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:16.792825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:17.688597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:18.658422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:19.474857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:20.483187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:21.373551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:22.402417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:23.264572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:24.237580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:13.918209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:14.884562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:15.963426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:16.865824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:17.762586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:18.724716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:19.547057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:20.553181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:21.444551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:22.474540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:23.339572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:24.443117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:13.997442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:14.964560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:16.033426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:16.939824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:17.967585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:18.793452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:19.620059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:20.625193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:21.517551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:22.549608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:23.415572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:24.514116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:14.081244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:15.043750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:16.112426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:17.014824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:18.044584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:18.860452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:19.695065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:20.700503image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:21.591680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:22.614608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:23.493572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:24.578114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:14.157493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:15.118750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:16.184420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:17.085782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:18.107987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:18.923460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:19.763877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:20.768428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:21.660681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:22.674615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:23.566150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:24.643115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:14.233569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:15.193750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:16.256420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:17.155782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:18.175239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:18.986469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:19.833638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:20.843550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:21.729999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:22.736624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:23.638150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:24.711113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:14.315757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:15.272750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:16.333420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:17.229781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:18.239430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:19.054476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:20.040180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:20.920552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:21.802999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:22.803620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-09-04T23:46:23.715150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-09-04T23:46:33.807267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-04T23:46:34.020658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-04T23:46:34.255188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-04T23:46:34.536372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-04T23:46:24.981044image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-04T23:46:25.897090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-04T23:46:26.432076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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01993496https://www.tvmaze.com/episodes/1993496/troe-iz-prostokvasino-2x41-bobrovyj-mehБобровый мех241.0regular2020-12-272020-12-27T00:00:00+00:0019.0NoneNaNhttps://static.tvmaze.com/uploads/images/medium_landscape/291/727650.jpghttps://static.tvmaze.com/uploads/images/original_untouched/291/727650.jpghttps://api.tvmaze.com/episodes/199349610892https://www.tvmaze.com/shows/10892/troe-iz-prostokvasinoТрое из ПростоквашиноAnimationRussian[Children, Family]Running7.014.01978-06-10Nonehttps://okko.tv/serial/prostokvashino12:00[]7.589NaN366.0OkkoRussian FederationRUAsia/KamchatkaNoneNoneNone255564.0Nonehttps://static.tvmaze.com/uploads/images/medium_portrait/51/128137.jpghttps://static.tvmaze.com/uploads/images/original_untouched/51/128137.jpgNone1662063139https://api.tvmaze.com/shows/10892https://api.tvmaze.com/episodes/2382770NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
11993442https://www.tvmaze.com/episodes/1993442/top-10-po-versii-seasonvarru-2x12-top-10-samyh-ozidaemyh-novinok-v-mire-serialovТОП-10 самых ожидаемых новинок в мире сериалов212.0regular2020-12-272020-12-27T00:00:00+00:007.0NoneNaNNaNNaNhttps://api.tvmaze.com/episodes/199344219628https://www.tvmaze.com/shows/19628/top-10-po-versii-seasonvarruТОП-10 по версии Seasonvar.ruTalk ShowRussian[]Running9.09.02015-11-27Nonehttp://seasonvar.ru/serial-12772-TOP-10_po_versii_Seasonvarru-1-season.html[]NaN28NaN56.0SeasonvarRussian FederationRUAsia/KamchatkaNoneNoneNoneNaNNonehttps://static.tvmaze.com/uploads/images/medium_portrait/69/173595.jpghttps://static.tvmaze.com/uploads/images/original_untouched/69/173595.jpgNone1661473967https://api.tvmaze.com/shows/19628https://api.tvmaze.com/episodes/2380279NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
21956341https://www.tvmaze.com/episodes/1956341/hero-return-1x12-episode-12Episode 12112.0regular2020-12-2710:002020-12-27T02:00:00+00:0015.0NoneNaNNaNNaNhttps://api.tvmaze.com/episodes/195634151471https://www.tvmaze.com/shows/51471/hero-returnHero ReturnAnimationChinese[Action, Anime, Science-Fiction]Running15.016.02020-10-18Nonehttps://v.qq.com/detail/q/q72jd29a3oxflsr.html10:00[Sunday]NaN82NaN104.0Tencent QQChinaCNAsia/Shanghaihttps://v.qq.com/NoneNoneNaNNonehttps://static.tvmaze.com/uploads/images/medium_portrait/279/698895.jpghttps://static.tvmaze.com/uploads/images/original_untouched/279/698895.jpg<p>Zero was mankind's first real superhero. Under his watch, countless other superheros appeared and followed in his footsteps. However, after 5 years of war, Zero disappeared without a trace.<br /><br />(Source: zeroscans)</p>1603467037https://api.tvmaze.com/shows/51471https://api.tvmaze.com/episodes/1956341NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
31988864https://www.tvmaze.com/episodes/1988864/swallowed-star-1x06-episode-6Episode 616.0regular2020-12-2710:002020-12-27T02:00:00+00:0021.0NoneNaNNaNNaNhttps://api.tvmaze.com/episodes/198886452178https://www.tvmaze.com/shows/52178/swallowed-starSwallowed StarAnimationChinese[Anime, Science-Fiction]RunningNaN21.02020-11-29Nonehttps://v.qq.com/detail/3/324olz7ilvo2j5f.html10:00[Wednesday]7.793NaN104.0Tencent QQChinaCNAsia/Shanghaihttps://v.qq.com/NoneNone392598.0Nonehttps://static.tvmaze.com/uploads/images/medium_portrait/286/715165.jpghttps://static.tvmaze.com/uploads/images/original_untouched/286/715165.jpg<p>One day, an unexplained RR virus appeared on the earth, drawing the world into disaster. Infected animals mutated into terrible monsters, invaded massively, and humans built walls around the destruction and established the base city as the last bastion for humans. The suffering that mankind has experienced during this period of time is known as the "Great Nirvana Period." Not only that, Luo Feng not only carried the burden of supporting the family but also to protect the human homeland, for the better survival and development of mankind, together with other justice warriors, to join hands against the fierce monsters. Under the desperate situation of the end, can Luo Feng and other warriors repel monsters and successfully protect the human world?</p>1661959856https://api.tvmaze.com/shows/52178https://api.tvmaze.com/episodes/2259040NaNhttps://api.tvmaze.com/episodes/2259041NaNNaNNaNNaNNaNNaNNaNNaNNaN
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461975189https://www.tvmaze.com/episodes/1975189/fancy-nancy-2x37-new-years-nancyNew Year's Nancy237.0regular2020-12-2712:002020-12-27T17:00:00+00:0015.0<p>Nancy wants to stay up until midnight on New Year's Eve but finds that staying awake is harder than she thought.</p>NaNNaNNaNhttps://api.tvmaze.com/episodes/197518934940https://www.tvmaze.com/shows/34940/fancy-nancyFancy NancyAnimationEnglish[Comedy, Adventure, Children]Running15.015.02018-07-13Nonehttps://disneynow.com/shows/fancy-nancy12:00[Sunday]NaN60NaN83.0DisneyNOWUnited StatesUSAmerica/New_YorkNoneNoneNone348820.0tt2229129https://static.tvmaze.com/uploads/images/medium_portrait/161/402691.jpghttps://static.tvmaze.com/uploads/images/original_untouched/161/402691.jpg<p><b>Fancy Nancy</b> centers around six-year-old Nancy, a girl who likes to be fancy in everything from her advanced vocabulary to her creative, elaborate attire. Excited to experience what the magnificent world has to offer, Nancy uses her ingenuity and imagination as she learns that while life doesn't always go as planned, it's important to celebrate the differences that make everyone unique.</p>1657374503https://api.tvmaze.com/shows/34940https://api.tvmaze.com/episodes/2358798NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
471981602https://www.tvmaze.com/episodes/1981602/peytons-places-2x05-terrell-owensTerrell Owens25.0regular2020-12-272020-12-27T17:00:00+00:0030.0NoneNaNNaNNaNhttps://api.tvmaze.com/episodes/198160243207https://www.tvmaze.com/shows/43207/peytons-placesPeyton's PlacesDocumentaryEnglish[History, Sports]Running30.029.02019-07-29Nonehttp://www.espn.com/watch/series/2043dd20-9cc0-4abe-b652-c8e7dfdfefa0/peyton-s-places[Sunday]NaN41NaN265.0ESPN+United StatesUSAmerica/New_YorkNoneNoneNone362863.0tt10241812https://static.tvmaze.com/uploads/images/medium_portrait/206/516930.jpghttps://static.tvmaze.com/uploads/images/original_untouched/206/516930.jpg<p><b>Peyton's Places</b> offers a fun, insightful tour through 100 years of football, following the sport and the league's rise to an American cultural touchstone. For nearly a year, Manning has crisscrossed the country, visiting the people and places that have played an important part in the making of the NFL—highlighting memorable events, teams, players, and trends over the past century.</p>1647692257https://api.tvmaze.com/shows/43207https://api.tvmaze.com/episodes/2043448NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
481975190https://www.tvmaze.com/episodes/1975190/fancy-nancy-2x38-nancys-gift-to-grandpaNancy's Gift to Grandpa238.0regular2020-12-2712:152020-12-27T17:15:00+00:0015.0<p>To cheer up Grandpa on a wintry day, Nancy draws a masterpiece in chalk on the driveway.</p>NaNNaNNaNhttps://api.tvmaze.com/episodes/197519034940https://www.tvmaze.com/shows/34940/fancy-nancyFancy NancyAnimationEnglish[Comedy, Adventure, Children]Running15.015.02018-07-13Nonehttps://disneynow.com/shows/fancy-nancy12:00[Sunday]NaN60NaN83.0DisneyNOWUnited StatesUSAmerica/New_YorkNoneNoneNone348820.0tt2229129https://static.tvmaze.com/uploads/images/medium_portrait/161/402691.jpghttps://static.tvmaze.com/uploads/images/original_untouched/161/402691.jpg<p><b>Fancy Nancy</b> centers around six-year-old Nancy, a girl who likes to be fancy in everything from her advanced vocabulary to her creative, elaborate attire. Excited to experience what the magnificent world has to offer, Nancy uses her ingenuity and imagination as she learns that while life doesn't always go as planned, it's important to celebrate the differences that make everyone unique.</p>1657374503https://api.tvmaze.com/shows/34940https://api.tvmaze.com/episodes/2358798NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
492234691https://www.tvmaze.com/episodes/2234691/one-mo-chance-1x11-one-mo-finaleOne Mo' Finale111.0regular2020-12-2720:002020-12-28T01:00:00+00:0050.0<p>Weeks of laughter, fighting and love have finally led to this. Will Yodela receive "One Mo' Chance", or will it be Yummy?</p>NaNhttps://static.tvmaze.com/uploads/images/medium_landscape/382/955842.jpghttps://static.tvmaze.com/uploads/images/original_untouched/382/955842.jpghttps://api.tvmaze.com/episodes/223469159398https://www.tvmaze.com/shows/59398/one-mo-chanceOne Mo' ChanceRealityEnglish[]RunningNaN47.02020-10-11Nonehttps://www.thezeusnetwork.com/one-mo-chance20:00[Sunday]NaN56NaN331.0ZeusUnited StatesUSAmerica/New_YorkNoneNoneNone390130.0tt14369906https://static.tvmaze.com/uploads/images/medium_portrait/382/955908.jpghttps://static.tvmaze.com/uploads/images/original_untouched/382/955908.jpg<p>From the breakdown of his relationship with the mother of his children, to the death of his brother and partner Real," the last few years have been personally tough for Kamal Chance Givens. However, the original Stallionaire is now ready to get back on his horse to give love another shot. During Chance return to reality television we'll watch as he goes it alone to find the true love of his life in this new dating competition series.</p>1649330631https://api.tvmaze.com/shows/59398https://api.tvmaze.com/episodes/2308893NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN